International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
In the past two decades, the technique of image processing has made its way into every aspect of today’s tech-savvy society. Its applications encompass a wide variety of specialized disciplines including medical imaging, machine vision, remote sensing and astronomy. Personal images captured by various digital cameras can easily be manipulated by a variety of dedicated image processing algorithms. Image restoration can be described as an important part of image processing technique. The basic objective is to enhance the quality of an image by removing defects and make it look pleasing. The method used to carry out the project was MATLAB software. Mathematical algorithms were programmed and tested for the result to find the necessary output. In this project mathematical analysis was the basic core. Generally the spatial and frequency domain methods were both important and applicable in different technologies. This project has tried to show the comparison between spatial and frequency domain approaches and their advantages and disadvantages. This project also suggested that more research have to be done in many other image processing applications to show the importance of those methods.
An adaptive method for noise removal from real world imagesIAEME Publication
The document summarizes an adaptive method for noise removal from real world images. It proposes modifying the bilateral filter, which considers both spatial and intensity distances between pixels. The modified filter adapts its strength based on the local noise level in the image. It estimates the smoothing parameter by analyzing noise strength factors within blocks of different sizes. This helps determine the appropriate block size to use for a given image region. The filter aims to remove Gaussian noise while preserving edges and details to enhance image quality. Experimental results show it performs well across different images for a wide range of noise levels.
The document discusses superresolution technology that can improve the resolution of infrared camera images. It begins by explaining the basic problem that small objects may be invisible or measured incorrectly in infrared images due to pixel size limitations. It then describes how superresolution works by using multiple images and deconvolution algorithms to effectively decrease pixel pitch by 1.6x and increase usable resolution also by 1.6x compared to normal images. Experimental results show that superresolution detects spatial frequencies about 50% higher than the camera's detector cutoff and improves temperature measurement accuracy compared to interpolation. The technology will be available as a software update for all current Testo infrared cameras.
The document discusses super resolution imaging techniques. Super resolution aims to enhance image resolution and clarity by processing multiple low resolution images to generate a single high resolution output image. It combines non-repetitive information from multiple low resolution images. Common approaches involve image registration, interpolation using techniques like nearest neighbor, bilinear, and bicubic interpolation, followed by noise removal. The techniques can help obtain higher resolution images for applications like satellite imaging, medical imaging, and surveillance.
The document proposes a new framework called structure-modulated sparse representation (SMSR) for single image super-resolution. Existing super-resolution methods increase artifacts and do not consider image structure. The proposed SMSR algorithm formulates an optimization problem using gradient priors and nonlocal sparsity to reconstruct high-resolution images. It exploits multi-scale similarity using multi-step magnification and ridge regression for initial estimation. The algorithm also incorporates gradient histogram preservation as a regularization term. Experimental results show the proposed method outperforms state-of-the-art methods in recovering fine structures and details from low-resolution images.
image denoising technique using disctere wavelet transformalishapb
This document discusses image denoising techniques using discrete wavelet transforms. It begins with an introduction and lists the objectives, goals, and types of noise that affect images. It then describes several denoising techniques including spatial filtering methods like mean, wiener and median filters as well as frequency domain filtering and wavelet domain filtering. The document provides block diagrams of the wavelet denoising process and evaluates performance of various denoising algorithms using metrics like PSNR and SSIM. It was implemented in MATLAB and concluded that wavelet thresholding provides significant improvement in image quality while preserving useful information.
This lecture is about particle image velocimetry technique. It include discussion about the basic element of PIV setup, image capturing, laser lights, synchronize and correlation analysis.
This document provides an overview of digital image processing and is divided into multiple parts. Part I discusses digital image fundamentals, image transforms, image enhancement, image restoration, image compression, and image segmentation. It introduces key concepts such as digital image systems, sampling and quantization, pixel relationships, and image transforms in both the spatial and frequency domains. Image processing techniques like filtering, histogram processing, and frequency domain filtering are also summarized.
1. The document discusses techniques for removing haze from digital images. It begins with an introduction to how haze forms and degrades image quality.
2. It then describes several categories of haze removal techniques, including multiple image dehazing methods that use multiple images and single image dehazing methods that rely on statistical assumptions. Specific techniques discussed include dark channel prior, guided image filtering, and bilateral filtering.
3. The document focuses on comparing different haze removal approaches and evaluating which methods produce higher quality results for single image dehazing.
발표자: 전석준(KAIST 박사과정)
발표일: 2018.8.
Super-resolution은 저해상도 이미지를 고해상도 이미지로 변환시키는 기술로 오랜기간 연구되어 온 주제입니다. 최근 딥러닝 기술이 적용됨에 따라 super-resolution 성능이 비약적으로 향상되었습니다. 저희는 스테레오 이미지를 이용하여 더 높은 해상도의 이미지를 얻는 기술을 개발하였습니다. 이에 관련 내용을 발표하고자 합니다.
1. Multi-Frame Super-Resolution
2. Learning-Based Super-Resolution
3. Stereo Imaging
4. Deep-Learning Based Stereo Super-Resolution
Deep learning for image super resolutionPrudhvi Raj
Using Deep Convolutional Networks, the machine can learn end-to-end mapping between the low/high-resolution images. Unlike traditional methods, this method jointly optimizes all the layers of the image. A light-weight CNN structure is used, which is simple to implement and provides formidable trade-off from the existential methods.
Review of Use of Nonlocal Spectral – Spatial Structured Sparse Representation...IJERA Editor
This document summarizes a research paper that proposes a new method for hyperspectral image restoration using nonlocal spectral-spatial structured sparse representation. The key points are:
1) It introduces using nonlocal similarity and spectral-spatial structure of hyperspectral images in sparse representation models. Nonlocal similarity means similar image patches can be represented by shared dictionary atoms, distinguishing true signals from noise.
2) Using 3D blocks that exploit spectral and spatial correlations, rather than 2D patches, for sparse coding. This better distinguishes true signals and noise.
3) A mixed Poisson-Gaussian noise model is used to handle signal-dependent and signal-independent noise present in hyperspectral images. Variance-fitting transformation
The document discusses image processing and provides information on several key topics:
1. Image processing can be grouped into compression, preprocessing, and analysis. Preprocessing improves image quality by reducing noise and enhancing edges. Analysis extracts numeric or graphical information for tasks like classification.
2. Images are 2D matrices of intensity values represented by pixels. Common digital formats include grayscale, RGB, and RGBA. Higher bit depths allow more intensity levels to be represented.
3. Basic measurements of images include spatial resolution in pixels per unit, bit depth determining representable intensity levels, and factors like saturation and noise.
This document discusses image fusion techniques at different levels of abstraction: pixel level, feature level, and decision level. It describes various fusion methods including numerical (e.g. multiplicative, Brovey), color related (e.g. IHS), statistical (e.g. PCA, Gram Schmidt), and feature level (e.g. Ehlers) techniques. Both qualitative (visual) and quantitative (statistical measures like RMSE, correlation coefficient, entropy) methods to assess fusion quality are outlined. Image fusion has applications in improving classification and displaying sharper resolution images.
The fourier transform for satellite image compressioncsandit
The document presents a new method for compressing satellite images using the Fourier transform and scalar quantization. The method involves taking the Fourier transform of the image, scalar quantizing the amplitude values, and encoding the results with run-length encoding and Huffman coding. Testing on satellite images and Lena showed compression ratios over 65% while maintaining good image quality after reconstruction.
This paper analyzed different haze removal methods. Haze causes trouble to
many computer graphics/vision applications as it reduces the visibility of the scene. Air light and
attenuation are two basic phenomena of haze. air light enhances the whiteness in scene and on
the other hand attenuation reduces the contrast. the colour and contrast of the scene is recovered
by haze removal techniques. many applications like object detection , surveillance, consumer
electronics etc. apply haze removal techniques. this paper widely focuses on the methods of
effectively eliminating haze from digital images. it also indicates the demerits of current
techniques.
Pan sharpening is a process that merges high-resolution panchromatic imagery with lower-resolution multispectral imagery to create a single high-resolution color image. It provides the best of both types of imagery - high spectral resolution and high spatial resolution. There are several common pan sharpening methods including Brovey, IHS, Esri, simple mean, and Gram-Schmidt transformations. Landsat 8 imagery for example consists of bands with 30m resolution except for the panchromatic band which has 15m resolution, and pan sharpening can be applied in GIS software like ArcGIS to improve the resolution of the multispectral bands.
This document proposes a remote sensing image fusion approach that combines the Brovey transform and wavelet transforms. The Brovey transform is used first to reduce spectral distortion, followed by a wavelet transform to reduce spatial distortion. The approach was tested on MODIS and SPOT data as well as ETM+ and SPOT data. Statistical analysis showed the proposed technique performed better than traditional fusion techniques like IHS, PCA, and the Brovey transform alone in terms of metrics like correlation coefficient, entropy, and structural similarity. Future work will focus on improving the technique and applying fused images to classification tasks.
This paper attempts to undertake study of
remove the Salt and Pepper Noise (SPN) from satellite
Image in Different Noise densities have been removed
between 10% to 60% by using Two types of software
programming such as MatLab Programming , ENVI
software, they are compared with one another. The
comparative study is conducted with the help of Mean
Square Errors (MSE) and Peak-Signal to Noise Ratio
(PSNR). So as to choose the base software for removal of
the SPN noise from satellite image.
This paper presents a frequency domain degraded
image restoration practical method. We call it practical wiener
filter. Using this filter, the value for K parameter of wiener
filter is determined experimentally that is so difficult and
time consuming. Furthermore, there is no any absolute remark
to claim that the obtained images by restoration process are
the best could be possible. In order to find a solution for this
problem, we use genetic algorithm to obtain the best value for
K. Therefore, this paper presents an image restoration method
which employs a Computer Aided Design (CAD) to image
restoration where there is no need to original safe image. It
means that, degraded image is as input and restored one is as
output of CAD. Simulation results confirm that this method
is successful and has executive ability in most applications.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
El resumen describe las características principales del aprendizaje en adultos. Los adultos son autónomos, independientes y capaces de aprender de manera autodirigida. Aprenden mejor cuando perciben que los conocimientos adquiridos son relevantes para su desarrollo personal y profesional. El aprendizaje en adultos se basa en la experiencia, el trabajo colaborativo, la retroalimentación continua y la elevación de la autoestima.
O documento descreve a IV Reunião Técnica Regional do Proinfância sobre Educação Infantil 100% Inclusiva realizada pelo MEC. O objetivo é apoiar 1.232 municípios que não possuem inclusão total de crianças com deficiência na educação infantil, por meio de consultoria técnica, formação de gestores, elaboração de planos de ação e monitoramento.
India is grappling with the highest ever current-account deficit, the broadest measure of trade, mainly because of its gold and crude oil imports, weakening the rupee to a record against the U.S. dollar. Over the past few years, investment in gold has increased significantly especially in bullion, bars, and securities related to bullion. www.unitedworld.edu.in
This document presents the design of an ultrahigh-speed electrical drive system capable of operating at 500,000 rpm with an output power of 100 W. A permanent magnet machine with a slotless winding is used to achieve low inductance and a high fundamental frequency. Three power electronic topologies are experimentally tested - a voltage source inverter with external inductors, a voltage source inverter with block commutation, and a current source inverter with external capacitors. The voltage source inverter with block commutation and additional DC-DC converter is selected as it results in the lowest system volume due to lower switching losses and a simpler control implementation.
This study assessed the enzymatic hydrolysis of proteins from the microalgae Chlorella pyrenoidosa and Spirulina sp. LEB 18 to produce protein hydrolysates. Three commercial proteases were used under different conditions to hydrolyze the microalgae proteins. The highest degrees of hydrolysis for Spirulina and Chlorella, respectively, were 55.31% and 52.9% and were obtained with 4 hours of reaction time using Protemax N200 protease. Statistical analysis showed that enzyme concentration, substrate concentration, and reaction time significantly affected the degree of hydrolysis. The results indicate it is possible to obtain protein hydrolysates with varying degrees of hydrolysis from microalgae
Este documento presenta información sobre el lanzamiento de Java 7. Explica que Java es un lenguaje de programación que se compila a bytecode, el cual puede ser ejecutado en cualquier implementación de la máquina virtual Java (JVM). También menciona que existen muchas implementaciones de la JVM para diferentes sistemas operativos y que el bytecode generado es portable entre ellas.
This document summarizes a study that analyzed the impact of lane width on passenger car unit (PCU) capacity under mixed traffic conditions on congested highways in India. Data on traffic composition and vehicle speeds was collected across 5 highways in Nagpur City. PCU values, which allow different vehicle types to be converted to a common unit, were calculated for different vehicle types based on the speed ratio and space occupancy ratio compared to a passenger car. PCU values varied by vehicle type and highway section depending on factors like traffic volume and lane width. The study found PCU values changed significantly with changes in these factors. Speed-volume relationships were also analyzed and typical curves were presented.
Este documento habla brevemente sobre un viaje a Chamonix, Francia. Menciona el uso de un auto y una telecabina panorámica para explorar el valle. También incluye un enlace a un sitio web.
The document discusses elementary set theory. It defines basic set theory concepts like sets, subsets, empty sets, finite and infinite sets, power sets, unions, intersections, and complements of sets. It provides examples to illustrate these concepts and properties like if A is a subset of B, then the power set of A is a subset of the power set of B. The document serves as an introduction to foundational concepts in set theory.
A Milennium Cestas de Alimentos é uma empresa sediada em Curitiba que oferece cestas de alimentos e ferramentas de marketing digital. O documento descreve seus fundadores, produtos, plano de compensação com vários bônus, e premiações para líderes que alcançarem metas de vendas no lançamento da empresa.
Este documento é uma homenagem de aniversário para uma amiga, destacando a importância dela em suas vidas e desejando-lhe felicidades, saúde e vitórias.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A Novel Blind SR Method to Improve the Spatial Resolution of Real Life Video ...IRJET Journal
This document proposes a novel blind super resolution method to improve the spatial resolution of real-life video sequences. The key aspects of the proposed method are:
1) It estimates blur without knowing the point spread function or noise statistics using a non-uniform interpolation super resolution method and multi-scale processing.
2) It uses a cost function with fidelity and regularization terms of a Huber-Markov random field to preserve edges and fine details in the reconstructed high resolution frames.
3) It performs masking to suppress artifacts from inaccurate motions, adaptively weighting the fidelity term at each iteration for faster convergence.
The method is tested on real-life videos with complex motions, objects, and brightness changes, showing
Image Denoising Based On Wavelet for Satellite Imagery: A ReviewIJMER
In this paper studied the use of wavelet and their family to denoising images. Satellite images
are extensively used in the field of RS and GIS for land possession, mapping use for planning and
decision support. As of many Satellite image having common problem i.e. noise which hold unwanted
information in an images. Different types of noise are addressing different techniques to denoising
remotely sense images. Noise within the remote sensing images identifying and denoising them is big
challenge before the researcher. Therefore we review wavelet for denoising of the remote sensing
images. Thus implementing wavelet is essential to get much higher quality denoising image. However,
they are usually too computationally demanding. In order to reduce the
FusIon - On-Field Security and Privacy Preservation for IoT Edge Devices: Con...jamesinniss
FusIon - On-Field Security and Privacy Preservation for IoT Edge Devices: Concurrent Defense Against Multiple types of Hardware Trojan Attacks
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Image fusion is a technique used to integrate a highresolution
panchromatic image with multispectral low-resolution
image to produce a multispectral high-resolution image, that
contains both the spatial information of the panchromatic highresolution
image and the color information of the multispectral
image .Although an increasing number of high-resolution images
are available along with sensor technology development, the
process of image fusion is still a popular and important method to
interpret the image data for obtaining a more suitable image for a
variety of applications, like visual interpretation and digital
classification. To get the complete information from the single
image we need to have a method to fuse the images. In the current
paper we are going to propose a method that uses hybrid of
wavelets for Image fusion.
Noise Level Estimation for Digital Images Using Local Statistics and Its Appl...TELKOMNIKA JOURNAL
In this paper, an automatic estimation of additive white Gaussian noise technique is proposed. This technique is built according to the local statistics of Gaussian noise. In the field of digital signal processing, estimation of the noise is considered as pivotal process that many signal processing tasks relies on. The main aim of this paper is to design a patch-based estimation technique in order to estimate the noise level in natural images and use it in blind image removal technique. The estimation processes is utilized selected patches which is most contaminated sub-pixels in the tested images sing principal component analysis (PCA). The performance of the suggested noise level estimation technique is shown its superior to state of the art noise estimation and noise removal algorithms, the proposed algorithm produces the best performance in most cases compared with the investigated techniques in terms of PSNR, IQI and the visual perception.
This document presents a redundant wavelet transform based method for single image super resolution. The proposed method decomposes a low resolution input image into subbands using redundant wavelet transform. The subbands are then interpolated using bicubic interpolation. Inverse redundant wavelet transform is applied to the interpolated subbands to generate the high resolution output image. The method is tested on various standard test images and wavelet types. Results show the proposed method achieves higher peak signal-to-noise ratios compared to conventional interpolation and discrete wavelet transform based super resolution methods.
An Improved Image Fusion Scheme Based on Markov Random Fields with Image Enha...Editor IJCATR
Image fusion is an important field in many image processing and analysis tasks in which fusion image data are acquired
from multiple sources. In this paper, we investigate the Image fusion of remote sensing images which are highly corrupted by salt and
pepper noise. In our paper we propose an image fusion technique based Markov Random Field (MRF). MRF models are powerful
tools to analyze image characteristics accurately and have been successfully applied to a large number of image processing
applications like image segmentation, image restoration and enhancement, etc.,. To de-noise the corrupted image we propose a
Decision based algorithm (DBA). DBA is a recent powerful algorithm to remove high-density Salt and Pepper noise using sheer
sorting method is proposed. Previously many techniques have been proposed to image fusion. In this paper experimental results are
shown our proposed Image fusion algorithm gives better performance than previous techniques.
Hyperspectral image mixed noise reduction based on improved k svd algorithmeSAT Publishing House
This document summarizes a proposed algorithm for reducing mixed noise in hyperspectral imagery. Hyperspectral images capture information across the electromagnetic spectrum and can be represented as three-dimensional tensors. The proposed method uses tensor decomposition and an improved K-SVD algorithm to adaptively detect and remove Gaussian noise, impulse noise, and mixtures of these from hyperspectral data. It formulates the noise removal problem using a weighted regularization approach and solves related optimization problems using techniques like singular value decomposition. The goal is to separate noise and noise-free components to reconstruct a cleaned hyperspectral image tensor.
This document summarizes research on using wavelet thresholding techniques for image denoising. It begins with an introduction to wavelets and wavelet transforms. Then, it reviews several related studies on wavelet-based image denoising methods. These include using statistical modeling of wavelet coefficients, incorporating human visual system models, and considering correlations between wavelet scales. The document concludes by describing adaptive thresholding and compression techniques for denoising images in the wavelet domain.
This document summarizes a research paper that proposes an improved deconvolution algorithm to estimate blood flow velocity in nailfold vessels more accurately. The paper describes limitations in existing algorithms related to blurring and proposes using deconvolution and other image enhancement techniques. Results show the new algorithm takes less time (20-21 seconds vs 42-43 seconds) and tracks particle movement more accurately, allowing more precise flow measurements. This helps diagnosis of diseases. Future work could involve additional segmentation and machine learning to further automate and improve reliability.
Intensify Denoisy Image Using Adaptive Multiscale Product ThresholdingIJERA Editor
This Paper presents a wavelet-based multiscale products thresholding scheme for noise suppression of magnetic resonance images. This paper proposed a method based on image de-noising and edge enhancement of noisy multidimensional imaging data sets. Medical images are generally suffered from signal dependent noises i.e. speckle noise and broken edges. Most of the noises signals appear from machine and environment generally not contribute to the tissue differentiation. But, the noise generated due to above mentioned reason causes a grainy appearance on the image, hence image enhancement is required. For the intent of image denoising, Adaptive Multiscale Product Thresholding based on 2-D wavelet transform is used. In this method, contiguous wavelet sub bands are multiplied to improve edge structure while reducing noise. In multiscale products, boundaries can be successfully distinguished from noise. Adaptive threshold is designed and forced on multiscale products as an alternative of wavelet coefficients or recognize important features. For the edge enhancement. Canny Edge Detection Algorithm is used with scale multiplication technique. Simulation results shows that the planned technique better suppress the Poisson noise among several noises i.e. salt & pepper, speckle noise and random noise. The Performance of Image Intesification can be estimate by means of PSNR, MSE.
Intensify Denoisy Image Using Adaptive Multiscale Product ThresholdingIJERA Editor
This Paper presents a wavelet-based multiscale products thresholding scheme for noise suppression of magnetic resonance images. This paper proposed a method based on image de-noising and edge enhancement of noisy multidimensional imaging data sets. Medical images are generally suffered from signal dependent noises i.e. speckle noise and broken edges. Most of the noises signals appear from machine and environment generally not contribute to the tissue differentiation. But, the noise generated due to above mentioned reason causes a grainy appearance on the image, hence image enhancement is required. For the intent of image denoising, Adaptive Multiscale Product Thresholding based on 2-D wavelet transform is used. In this method, contiguous wavelet sub bands are multiplied to improve edge structure while reducing noise. In multiscale products, boundaries can be successfully distinguished from noise. Adaptive threshold is designed and forced on multiscale products as an alternative of wavelet coefficients or recognize important features. For the edge enhancement. Canny Edge Detection Algorithm is used with scale multiplication technique. Simulation results shows that the planned technique better suppress the Poisson noise among several noises i.e. salt & pepper, speckle noise and random noise. The Performance of Image Intesification can be estimate by means of PSNR, MSE.
This document discusses different techniques for image denoising using wavelet thresholding. It begins with an introduction to image denoising and the wavelet transform approach. Then it describes various thresholding methods used in wavelet-based image denoising, including hard, soft, universal, improved, Bayes shrink, and neigh shrink thresholding. It also reviews prior literature comparing these different techniques. Finally, it presents simulated results on test images comparing the performance of universal hard thresholding and improved thresholding based on mean squared error and peak signal-to-noise ratio metrics under varying levels of additive white Gaussian noise. The improved thresholding method achieved better denoising performance according to the quantitative metrics.
This document discusses and compares different thresholding techniques for image denoising using wavelet transforms. It introduces the concept of image denoising using wavelet transforms, which involves applying a forward wavelet transform, estimating clean coefficients using thresholding, and applying the inverse transform. It then describes several common thresholding methods - hard, soft, universal, improved, Bayes shrink, and neigh shrink. Simulation results on test images corrupted with additive white Gaussian noise show that the proposed improved thresholding technique achieves lower MSE and higher PSNR than the universal hard thresholding method, demonstrating better noise removal performance while preserving image details.
Mr image compression based on selection of mother wavelet and lifting based w...ijma
Magnetic Resonance (MR) image is a medical image technique required enormous data to be stored and
transmitted for high quality diagnostic application. Various algorithms have been proposed to improve the
performance of the compression scheme. In this paper we extended the commonly used algorithms to image
compression and compared its performance. For an image compression technique, we have linked different
wavelet techniques using traditional mother wavelets and lifting based Cohen-Daubechies-Feauveau
wavelets with the low-pass filters of the length 9 and 7 (CDF 9/7) wavelet transform with Set Partition in
Hierarchical Trees (SPIHT) algorithm. A novel image quality index with highlighting shape of histogram
of the image targeted is introduced to assess image compression quality. The index will be used in place of
existing traditional Universal Image Quality Index (UIQI) “in one go”. It offers extra information about
the distortion between an original image and a compressed image in comparisons with UIQI. The proposed
index is designed based on modelling image compression as combinations of four major factors: loss of
correlation, luminance distortion, contrast distortion and shape distortion. This index is easy to calculate
and applicable in various image processing applications. One of our contributions is to demonstrate the
choice of mother wavelet is very important for achieving superior wavelet compression performances based
on proposed image quality indexes. Experimental results show that the proposed image quality index plays
a significantly role in the quality evaluation of image compression on the open sources “BrainWeb:
Simulated Brain Database (SBD) ”.
Abstract: Primarily due to the progresses in super resolution imagery, the methods of segment-based image analysis for generating and updating geographical information are becoming more and more important. This work presents a image segmentation based on colour features with K-means clustering. The entire work is divided into two stages. First enhancement of color separation of satellite image using de correlation stretching is carried out and then the regions are grouped into a set of five classes using K-means clustering algorithm. At first, the spatial data is concentrated focused around every pixel, and at that point two separating procedures are added to smother the impact of pseudoedges. What's more, the spatial data weight is built and grouped with k-means bunching, and the regularization quality in every district is controlled by the bunching focus esteem. The exploratory results, on both reenacted and genuine datasets, demonstrate that the proposed methodology can adequately lessen the pseudoedges of the aggregate variety regularization in the level.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Image resolution enhancement by using wavelet transform 2IAEME Publication
This document discusses techniques for enhancing the resolution of digital images using wavelet transforms. It proposes a method that uses both stationary wavelet transform (SWT) and discrete wavelet transform (DWT) to decompose an input image into subbands, which are then interpolated using Lanczos interpolation before being combined via inverse DWT. The method is shown to achieve higher peak signal-to-noise ratios than traditional interpolation techniques like bilinear and bicubic interpolation as well as other wavelet-based super resolution methods, demonstrating its effectiveness for image resolution enhancement.
MR Image Compression Based on Selection of Mother Wavelet and Lifting Based W...ijma
Magnetic Resonance (MR) image is a medical image technique required enormous data to be stored and
transmitted for high quality diagnostic application. Various algorithms have been proposed to improve the
performance of the compression scheme. In this paper we extended the commonly used algorithms to image
compression and compared its performance. For an image compression technique, we have linked different
wavelet techniques using traditional mother wavelets and lifting based Cohen-Daubechies-Feauveau
wavelets with the low-pass filters of the length 9 and 7 (CDF 9/7) wavelet transform with Set Partition in
Hierarchical Trees (SPIHT) algorithm. A novel image quality index with highlighting shape of histogram
of the image targeted is introduced to assess image compression quality. The index will be used in place of
existing traditional Universal Image Quality Index (UIQI) “in one go”. It offers extra information about
the distortion between an original image and a compressed image in comparisons with UIQI. The proposed
index is designed based on modelling image compression as combinations of four major factors: loss of
correlation, luminance distortion, contrast distortion and shape distortion. This index is easy to calculate
and applicable in various image processing applications. One of our contributions is to demonstrate the
choice of mother wavelet is very important for achieving superior wavelet compression performances based
on proposed image quality indexes. Experimental results show that the proposed image quality index plays
a significantly role in the quality evaluation of image compression on the open sources “BrainWeb:
Simulated Brain Database (SBD) ”.
Survey Paper on Image Denoising Using Spatial Statistic son PixelIJERA Editor
This document summarizes research on image denoising using spatial statistics on pixel values. It begins with an abstract describing an approach that uses adaptive anisotropic weighted similarity functions between local neighborhoods derived from Mexican Hat wavelets to improve perceptual quality over existing methods. It then reviews literature on various denoising techniques including non-local means, non-uniform triangular partitioning, undecimated wavelet transforms, anisotropic diffusion, and support vector regression. Key types of image noise like Gaussian, salt and pepper, Poisson, and speckle noise are described. Limitations of blurring and noise in digital images are discussed. In conclusion, the document provides an overview of image denoising research using spatial and transform domain techniques.
Latest Tech Trends Series 2024 By EY IndiaEYIndia1
Stay ahead of the curve with our comprehensive Tech Trends Series! Explore the latest technology trends shaping the world today, from the 2024 Tech Trends report and top emerging technologies to their impact on business technology trends. This series delves into the most significant technological advancements, giving you insights into both established and emerging tech trends that will revolutionize various industries.
How UiPath Discovery Suite supports identification of Agentic Process Automat...DianaGray10
📚 Understand the basics of the newly persona-based LLM-powered Agentic Process Automation and discover how existing UiPath Discovery Suite products like Communication Mining, Process Mining, and Task Mining can be leveraged to identify APA candidates.
Topics Covered:
💡 Idea Behind APA: Explore the innovative concept of Agentic Process Automation and its significance in modern workflows.
🔄 How APA is Different from RPA: Learn the key differences between Agentic Process Automation and Robotic Process Automation.
🚀 Discover the Advantages of APA: Uncover the unique benefits of implementing APA in your organization.
🔍 Identifying APA Candidates with UiPath Discovery Products: See how UiPath's Communication Mining, Process Mining, and Task Mining tools can help pinpoint potential APA candidates.
🔮 Discussion on Expected Future Impacts: Engage in a discussion on the potential future impacts of APA on various industries and business processes.
Enhance your knowledge on the forefront of automation technology and stay ahead with Agentic Process Automation. 🧠💼✨
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Denoising Of Hyperspectral Image
Ashumati Dhuppe* ,Supriya P. Gaikwad** And Prof. Vijay R. Dahake**
* Dept. of Electronics and Telecommunication, Ramrao Adik Institute of Technology, Navi Mumbai, Mumbai
University, India.
** Dept. of Electronics and Telecommunication, Ramrao Adik Institute of Technology, Navi Mumbai, Mumbai
University, India.
ABSTRACT
The amount of noise included in a Hyperspectral images limits its application and has a negative impact on
Hyperspectral image classification, unmixing, target detection, so on. Hyperspectral imaging (HSI) systems can
acquire both spectral and spatial information of ground surface simultaneously and have been used in a variety
of applications such as object detection, material identification, land cover classification etc.
In Hyperspectral images, because the noise intensity in different bands is different, to better suppress the noise
in the high noise intensity bands & preserve the detailed information in the low noise intensity bands, the
denoising strength should be adaptively adjusted with noise intensity in different bands. We propose a
Hyperspectral image denoising algorithms employing a spectral spatial adaptive total variation (TV) model, in
which the spectral noise difference & spatial information differences are both considered in the process of noise
reduction.
To reduce the computational load in the denoising process, the split Bergman iteration algorithm is employed to
optimize the spectral spatial Hyperspectral TV model and accelerate the speed of Hyperspectral image
denoising. A number of experiments illustrate that the proposed approach can satisfactorily realize the spectral
spatial adaptive mechanism in the denoising process, and superior denoising result are provided.
Keywords – Hyperspetral images denoising, spaital adaptive, spectral adaptive, spectral spatial adaptive
Hyperspectral total variation, split Bregman iteration.
I. INTRODUCTION
Hyperspectral image (HIS) analysis has matured into
one of most powerful and fastest growing
technologies in the field of remote sensing. A
Hyperspectral remote sensing system is designed to
faithfully represent the whole imaging process on the
premise of reduced description complexity. It can
help system users understand the Hyperspectral
imaging (HSI) system better and find the key
contributors to system performance so as to design
more advanced Hyperspectral sensors and to
optimize system parameters. A great number of
efficient and cost-effective data can also be produced
for validation of Hyperspectral data processing. As
both sensor and processing systems become
increasingly complex, the need for understanding the
impact of various system parameters on performance
also increases.
Hyperspectral images contain a wealth of
data, interpreting them requires an understanding of
exactly what properties of ground materials we are
trying to measure, and how they relate to the
measurements actually made by the Hyperspectral
sensor.
The Hyperspectral data provide contiguous
of noncontiguous 10-nm bands throughout the 400-
2500-nm region of electromagnetic spectrum and,
hence have potential to precisely discriminate
different land cover type using the abundant spectral
information.such identification is of great
significance for detecting minerals,precision farming
urban planning, etc
The existence of noise in a Hyperspectral
image not only influences the visual effect of these
images but also limits the precision of subsequent
processing, for example, in classification, unmixing
subpixel mapping, target detection, etc. Therefore, it
is critical to reduce the noise in the Hyperspectral
image and improve its quality before the subsequent
image interpretation processes. In recent decades,
many Hyperspectral image denoising algorithms have
been proposed. For example, Atkinson [6] proposed a
wavelet-based Hyperspectral image denoising
algorithm, and Othman and Qian [7] proposed a
hybrid spatial–spectral derivative-domain wavelet
shrinkage noise reduction (HSSNR) approach. The
latter algorithm resorts to the spectral derivative
domain, where the noise level is elevated, and
benefits from the dissimilarity of the signal regularity
in the spatial and the spectral dimensions of
Hyperspectral images. Chen and Qian [8], [9]
proposed to perform dimension reduction and
Hyperspectral image denoising using wavelet
shrinking and principal component analysis (PCA).
Qian and Lévesque [10] evaluated the HSSNR
algorithm on unmixing-based Hyperspectral image
RESEARCH ARTICLE OPEN ACCESS
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target detection. Recently, Chen [11] proposed a new
Hyperspectral image denoising algorithm by adding a
PCA transform before using wavelet shrinkage; first,
a PCA transform was implemented on the original
Hyperspectral image, and then, the low-energy PCA
output channel was de-noised with wavelet shrinkage
denoising processes. Another type of filter-based
Hyperspectral image denoising algorithm is based on
a tensor model, which was proposed by Letexier and
Bourennane [12], and has been evaluated in
Hyperspectral image target detection [13] and
classification [14].Recently, afilter-based
Hyperspectral image denoising approach using
anisotropic diffusion has also been proposed [15]–
[17].As Hyperspectral images have dozens or even
hundreds of bands, and the noise intensity in each
band is different, the denoising strength should be
adaptively adjusted with the noise intensity in each
band. In another respect, with the improvements in
sensor technology, the development and increasing
use of images with both high spatial and spectral
resolutions have received more attention.
II. 2. METHOD OF
IMPLEMENTATION
2.1 MAP Hyperspectral Image Denoising Model
2.1.1 Hyperspectral Noise Degradation Model.
Assuming that we have an original
Hyperspectral image, and the degradation noise is
assumed to be additive noise, the noise degradation
model of the Hyperspectral image can be written as
f = u + n (1)
where u = [u1 ,u2, … … . . uj……, uB] is the original clear
Hyperspectral image, with the size of M × N × B, in
which M represents the samples of the image, N
stands for the lines of the image, and B is the number
of bands. f = [f1 ,f2, … … . . fj……, fB] The noise
degradation image which also of size M × N × B, and
n = [n1 ,n2, … … . . nj……, nB ] is the additive noise with
the same size as u and f. The degradation process
Hyperspectral image is shown in “Fig.1”.
Fig. 1. Noise degradation process of the
Hyperspectral image.
2.1.2 MAP Denoising Model
The MAP estimation theory, which inherently
includes prior constraints in the form of prior
probability density functions, has been attracting
attention and enjoying Increasing popularity. It has
been used to solve many image processing problems,
which can be formed as ill-posed and inverse
problems, such as image denoising, destriping and
Inpainting, super resolution reconstruction, and
others. Therefore, because the Hyperspectral image
denoising process is an inverse and ill-posed
problem. The MAP estimation theory is used to solve
it. Based on the MAP estimation theory, the
denoising model for a Hyperspectral image can be
represented as the following constrained least squares
problem
u = argmin ∥B
j=1 uj − fj ∥
2
2
+ λR u (2)
∥B
j=1 uj − fj ∥
2
2
Which stands for the fidelity
between the observed noisy image and the original
clear image, and R (u) is the regularization item,
which gives a prior model of the original clear
Hyperspectral image λ is the regularization
parameter, which controls the tradeoff between the
data fidelity and regularization item.
2.2 SSAHTV MODEL
2.2.1 TV Model
The TV model was first proposed by Rudin to solve
the gray-level image denoising problem because of
its property of effectively preserving edge
information. For a gray-level image u, the TV model
is defined as follows:
TV(u) = ∇i
h
u
2
+ ∇i
v
u 2
t (3)
Where ∇i
h
and ∇i
v
are linear operators corresponding to
the horizontal and vertical first-Differences
respectively at pixel i.
Where ∇i
h
u = ui − ub i
ui − ub i
r(i) and b(i) represent the nearest neighbor to the right
and below the pixel.
2.2.2Spectral Adaptive Hyperspectral model
The simplest way of extending the TV
model to Hyperspectral images is by a band-by-band
manner, which means that, for every band, the TV
model is defined like the gray-level image TV model
in (3), and then, the TV model of each band is added
together. This simple band-by-band Hyperspectral
TV model is defined as follow
HTV u 1 = TV uj (4)
B
j=1
Where u is the Hyperspectral image, which has the
formation of u =u1 ,u2, … … . . uj……, uB] and uj stands
for the jth
band of the Hyperspectral image. If we
incorporate the band-by-band. For (5), the Euler–
Lagrange equation is written as band Hyperspectral
TV model in (5) into the Regularization model in (2),
the denoising model can
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u = argmin ∥B
j=1 uj – fj ∥
2
2
+ λ TV uj
B
j=1 (5)
Where
uj − fi − λ∇.
∇uj
∇uj
= 0. (6)
From (6), it means that every band is separately
denoised by the single-band TV model, which will
cause the following drawback. For a Hyperspectral
image, because the noise intensity of each band is
almost always different, the denoising strength
should also be different in each band. However, in
(6), if we use the same regularization parameter λ for
all the bands, which means that the regularization
strength of each band is equal, to define the
Hyperspectral TV model, which has the following
formation:
HTV(u)2 = ∇ij uj
2B
j=1
MN
j=1 (7)
∇ij uj
2
= ∇ij
h
u
2
+ ∇ij
v
u
2
(8)
Where MN is the total number of pixels in one
Hyperspectral band and B is the total number of
bands. ∇ij
h
and ∇ij
v
are linear operators corresponding
to the horizontal and vertical first order differences at
the ith
pixel in the jth
band, respectively. To more
clearly explain the formation of the Hyperspectral TV
model, we use “Fig. 2” to illustrate it. The reason
why the Hyperspectral TV model defined in (7) can
realize the spectral adaptive property in the denoising
process can be explained as follows. If we
incorporate the Hyperspectral TV model in (7) into
(2), it will become
Fig.2 Formulation process of the Hyperspectral
TV model.
u = argmin ∥
B
j=1
uj – fj ∥
2
2
+ λ (∇ij 𝑢)2
𝐵
𝑗 =1
𝑀𝑁
𝑖=1
(9)
In (9), if we take the derivative for uj, the Euler–
Lagrange equation of (9) can be written as
uj − fi − λ∇.
∇uj
∇uj
B
j=1 2
= 0 (10)
To give a clearer illustration, (10) is written the
following way:
uj − fi − λ∇.
∇uj
∇ujB
j=1 2
.
∇uj
∇uj
= 0 (11)
Compared with (6), we can see that an adjustment
parameter ∇uj ∇uj
2B
j=1 is added in (11) to
automatically adjust the denoising strength of each
band. For the high noise-intensity bands, as
∇uj ∇uj
2B
j=1 has a large value, the denoising
strength for these bands will be powerful. Inversely,
for the bands with low-intensity noise, as
∇uj ∇uj
2B
j=1 has a small value, a weak
denoising strength will be used on them.
2.2.3 SSAHTV model
Spectral adaptive property of the
Hyperspectral TV model is analyzed, another
important problem is how to realize the spatial
adaptive aspect in the process of denoising, which
means how to adjust the denoising strength in
different pixel locations in the same band, with the
spatial structure distribution. The spatially adaptive
mechanism can be described as follows. For a
Hyperspectral image u, we first calculate the gradient
information of every band using the following:
∇uj = (∇huj )2 + (∇vuj )2 (12)
Where ∇h
uj and ∇v
ujare the horizontal and vertical
first order gradients of uj and (∇h
uj) 2 and (∇v
uj)2
represent the squares of each element of ∇h
ujand
∇v
uj. Next, the gradient information of every band is
added together, and the square root is taken o f each
element of the sum
G = (∇uj)2B
j=1 (13)
Let Gi be the ith
element of vector G, and a weight
parameter Wi , which controls the interband
denoising strength, is defined in the following:
Ti =
1
1+μGi
(14)
Wi =
Ti
T
T =
Ti
MN
i=1
MN
(15)
Where μ is a constant parameter, the range of
parameter τ is between [0, 1], and τ is the mean value
of Ti . To make the process of denoising spatially
adaptive, the parameter Wi is added to the
Hyperspectral TV model in (7), and the SSAHTV
model is defined as
SSAHTV (u) = Wi (∇ij
B
j=1
MN
i=1 u)2
(16)
Where Wi represents the spatial weight of the ith
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pixel in the Hyperspectral TV model. With the
definition in (16), it is clearly seen that, for pixels in
smooth regions, the value of the gradient information
Wi will be small and the spatial weight Wi will have
a high value. Therefore, a powerful denoising
strength will be used for these pixels, and the noise in
the smooth areas will be suppressed better.
Conversely, for the pixels in the edge and texture
areas. The value of Gi will be large, and the spatial
parameter Wi will have a small value. Thus, a weak
denoising strength will be used for them, and the
edge and detailed information will be preserved.
With the SSAHTV model, the final MAP denoising
model used can be written as
u = argmin ∥
B
j=1
uj – fj
∥
2
2
+ λ Wi (∇ij
B
j=1
MN
i=1
u)2
(17)
III. Conclusion
Spectral Spatial Adaptive TV Hyperspectral
image denoising algorithm, in which the noise
distribution difference between different bands and
the spatial information difference between different
pixels are both considered in the process of
denoising. First, a MAP-based Hyperspectral
denoising model is constructed, which consists of
two items:1) The data fidelity item and 2) The
regularization item. Then, for the regularization item,
an SSAHTV model is proposed, which can control
the denoising strength between different bands and
pixels with different spatial properties. In different
bands, a large denoising strength is enforced in a
band with high noise intensity, and conversely, a
small denoising strength is used in bands with low-
intensity noise. At the same time, in different spatial
property regions in the Hyperspectral image, a large
denoising strength is used in smooth areas to
completely suppress noise, and a small denoising
strength is used in the edge areas to preserve detailed
information. Finally, the split Bergman iteration
algorithm is used to optimize the spectral–spatial
adaptive TV Hyperspectral image denoising model in
order to reduce the high computation load in the
process of Hyperspectral image denoising.
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