Noise adaptive switching median-based filter for impulse noise removal from extremely corrupted images
ABSTRACT In this study an approach to impulse noise removal is presented. The introduced algorithm is a switching filter which identifies the noisy pixels and then corrects them by using median filter. In order to identify pixels corrupted by noise an analysis of local intensity extrema is applied. Comprehensive analysis of the algorithm performance [in terms of peak signal-to-noise ratio (PSNR) and Structural SIMilarity (SSIM) index] is presented. Results obtained on wide range of noise corruption (up to 98%) are shown and discussed. Moreover, comparison with well-established methods for impulse noise removal is provided. Presented results reveal that the proposed algorithm outperforms other approaches to impulse noise removal and its performance is close to ideal switching median filter. For high noise densities, the method correctly detects up to 100% of noisy pixels.
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ABSTRACT: In this work, we present a new method of noise removal which is applied on images corrupted by impulse noise. This new algorithm has a good trade-off between quantitative and qualitative properties of the recovered image and the computation time. In this new method, the corrupted pixels are replaced by using a median filter or, they are estimated by their neighbors’ values. Our proposed method shows better results especially in very high density noisy images than Standard Median Filter (SMF), Adaptive Median Filter (AMF) and some other well-known filters for removing impulse noise. Experimental results show the superiority of the proposed algorithm in measures of PSNR and SSIM, specifically when the image is corrupted with more than 90% impulse noise.Scientia Iranica 12/2012; 19(6):1738–1745. · 0.54 Impact Factor