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Performance Evaluation of Noise Reduction Filters for Color Images through Normalized Color Difference (NCD) Decomposition
Author(s) -
F. Russo
Publication year - 2014
Publication title -
isrn machine vision
Language(s) - English
Resource type - Journals
eISSN - 2090-780X
pISSN - 2090-7796
DOI - 10.1155/2014/579658
Subject(s) - noise reduction , distortion (music) , artificial intelligence , noise (video) , computer vision , computer science , gaussian noise , residual , filter (signal processing) , salt and pepper noise , color difference , reduction (mathematics) , median filter , mathematics , pattern recognition (psychology) , image (mathematics) , algorithm , image processing , computer network , amplifier , bandwidth (computing) , geometry
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.

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