scholarly journals Performance Evaluation of Noise Reduction Filters for Color Images through Normalized Color Difference (NCD) Decomposition

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Fabrizio Russo
Keyword(s):  
Performance Evaluation ◽  
Image Denoising ◽  
Computer Simulations ◽  
Gaussian Noise ◽  
Reference Method ◽  
Color Difference ◽  
Color Images ◽  
Actual Performance ◽  
Residual Noise ◽  
Full Reference

Removing noise without producing image distortion is the challenging goal for any image denoising filter. Thus, the different amounts of residual noise and unwanted blur should be evaluated to analyze the actual performance of a denoising process. In this paper a novel full-reference method for measuring such features in color images is presented. The proposed approach is based on the decomposition of the normalized color difference (NCD) into three components that separately take into account different classes of filtering errors such as the inaccuracy in filtering noise pulses, the inaccuracy in reducing Gaussian noise, and the amount of collateral distortion. Computer simulations show that the proposed method offers significant advantages over other measures of filtering performance in the literature, including the recently proposed vector techniques.

scholarly journals Image Denoising in Wavelet Domain with Filtering and Thresholding

2018 ◽  
Vol 7 (3.34) ◽  
pp. 327
Author(s):  
K Sumathi ◽  
Ch Hima Bindu
Keyword(s):  
Mutual Information ◽  
Image Denoising ◽  
Gaussian Noise ◽  
Color Images ◽  
Wavelet Domain ◽  
Wavelet Coefficients ◽  
Noisy Images

In this paper, the proposed method is implemented for removal of salt & pepper and Gaussian noise of black & white & color images toacquire the quality output. In this work initially wavelet coefficients are extracted for noisy images. Later apply denoise filteringtechnique on the high transform sub bands of noisy images (either color/ B & W) using new laplacian filters with 4 directions. Finallythreshold of an image is generated to extract denoisy coefficients. At last inverse of above subband coefficients can give denoise imagefor further processing. The proposed method is verified against various B & W/color images and it gives a better PSNR (Peak Signal toNoise Ratio) & MI (Mutual Information). These values are compared with different noise densities and analyzed visually.

Color Difference Evaluation Based on Image Color Appearance Model

2012 ◽  
Vol 200 ◽  
pp. 637-640
Author(s):  
Jing Liang ◽  
Yong Bin Zhao ◽  
Hui Gao
Keyword(s):  
Calculation Method ◽  
Color Difference ◽  
Color Images ◽  
Color Appearance ◽  
Appearance Model ◽  
Difference Formula ◽  
Difference Calculation

The iCAM (image color appearance model) as the most advanced modern color appearance model is constantly being put into use. In color images industry, the evaluation of the color difference is significant. This article will focus on color difference formula of image color appearance model and analysis its reasonable color difference calculation method, which reflects the advantages of image color appearance model.

Image Denoising via Bayesian Estimation of Statistical Parameter Using Generalized Gamma Density Prior in Gaussian Noise Model

2015 ◽  
Vol 14 (02) ◽  
pp. 1550017
Author(s):  
Pichid Kittisuwan
Keyword(s):  
Image Processing ◽  
Image Denoising ◽  
Gaussian Noise ◽  
Classical Problem ◽  
Statistical Parameter ◽  
Noise Model ◽  
Natural Image ◽  
Bayesian Techniques ◽  
Generalized Gamma ◽  
Gamma Density

The application of image processing in industry has shown remarkable success over the last decade, for example, in security and telecommunication systems. The denoising of natural image corrupted by Gaussian noise is a classical problem in image processing. So, image denoising is an indispensable step during image processing. This paper is concerned with dual-tree complex wavelet-based image denoising using Bayesian techniques. One of the cruxes of the Bayesian image denoising algorithms is to estimate the statistical parameter of the image. Here, we employ maximum a posteriori (MAP) estimation to calculate local observed variance with generalized Gamma density prior for local observed variance and Laplacian or Gaussian distribution for noisy wavelet coefficients. Evidently, our selection of prior distribution is motivated by efficient and flexible properties of generalized Gamma density. The experimental results show that the proposed method yields good denoising results.

scholarly journals EXPERIMENTS FOR ACTUAL PERFORMANCE EVALUATION OF ROOM AIR-CONDITIONERS IN AN EXPERIMENTAL HOUSE

2013 ◽  
Vol 78 (688) ◽  
pp. 497-505
Author(s):  
Daisuke SUMIYOSHI ◽  
Takashi OGINO ◽  
Hisashi MIURA ◽  
Akinori HOSOI ◽  
Takao SAWACHI
Keyword(s):  
Performance Evaluation ◽  
Actual Performance ◽  
Air Conditioners

scholarly journals From Insect Vision to a Novel Bio-Inspired Algorithm for Image Denoising

2020 ◽  
Author(s):  
Manfred Hartbauer
Keyword(s):  
Image Denoising ◽  
Color Images ◽  
Insect Vision ◽  
Night Vision ◽  
Primitive Type ◽  
Noise Estimate ◽  
Local Means ◽  
Static Images ◽  
Image Details ◽  
Novel Algorithm

Night active insects inspired the development of image enhancement methods that uncover the information contained in dim images or movies. Here, I describe a novel bionic night vision (NV) algorithm that operates in the spatial domain to remove noise from static images. The parameters of this NV algorithm can be automatically derived from global image statistics and a primitive type of noise estimate. In a first step, luminance values were ln-transformed, and then adaptive local means’ calculations were executed to remove the remaining noise without degrading fine image details and object contours. Its performance is comparable with several popular denoising methods and can be applied to grey-scale and color images. This novel algorithm can be executed in parallel at the level of pixels on programmable hardware.

scholarly journals Medical Image Denoising Algorithm Based on Sparse Nonlocal Regularized Weighted Coding and Low Rank Constraint

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Quan Yuan ◽  
Zhenyun Peng ◽  
Zhencheng Chen ◽  
Yanke Guo ◽  
Bin Yang ◽  
...  
Keyword(s):  
Image Denoising ◽  
Gaussian Noise ◽  
Medical Image ◽  
Impulse Noise ◽  
Medical Images ◽  
Noise Removal ◽  
White Gaussian Noise ◽  
Low Rank ◽  
Rank Constraint ◽  
Nonlocal Regularization

Medical image information may be polluted by noise in the process of generation and transmission, which will seriously hinder the follow-up image processing and medical diagnosis. In medical images, there is a typical mixed noise composed of additive white Gaussian noise (AWGN) and impulse noise. In the conventional denoising methods, impulse noise is first removed, followed by the elimination of white Gaussian noise (WGN). However, it is difficult to separate the two kinds of noises completely in practical application. The existing denoising algorithm of weight coding based on sparse nonlocal regularization, which can simultaneously remove AWGN and impulse noise, is plagued by the problems of incomplete noise removal and serious loss of details. The denoising algorithm based on sparse representation and low rank constraint can preserve image details better. Thus, a medical image denoising algorithm based on sparse nonlocal regularization weighted coding and low rank constraint is proposed. The denoising effect of the proposed method and the original algorithm on computed tomography (CT) image and magnetic resonance (MR) image are compared. It is revealed that, under different σ and ρ values, the PSNR and FSIM values of CT and MRI images are evidently superior to those of traditional algorithms, suggesting that the algorithm proposed in this work has better denoising effects on medical images than traditional denoising algorithms.

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