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: Image signal is corrupted by various noises in image processing, many studies are being accomplished to restore those images. In this paper, we proposed a cascade filter algorithm for removing random valued impulse noise. The algorithm consists two steps that noise detection and noise elimination. Variance of filtering mask and center pixel variance are calculated for noise detection, and the noise pixel is replaced by estimated value which first apply switching self adaptive weighted median filter and finally processed by modified weight filter. Considering the proposed algorithm only remove noise and preserve the uncorrupted information that the algorithm can not only remove noise well but also preserve edge.Journal of the Korea Institute of Information and Communication Engineering. 01/2012; 16(3).
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ABSTRACT: Image processing has become an area of dynamic research with numerous applications in various fields. With the improvements gained in technologies related to multicomputer, multiprocessor and, more recently, to General Purpose Computing on Graphics Processing Units (GPGPUs), the parallelization of computational image processing techniques has gained extraordinary prominence. This parallelization is crucial for the use of such techniques in applications that have strong demands in terms of processing time, so that even more complex computational algorithms can be used, as well as their use on images of higher resolution. In this work, the parallelization in GPGPU of a recent image smoothing method based on a variation model is described and discussed. This method was proposed by Jin and Yang and is in-demand due to its computation time, and its use with high resolution images. The results obtained are very promising, revealing a considerable gain in terms of computational speed.International Conference on Computational and Experimental Biomedical Sciences, Ponta Delgada, Azores Islands; 10/2013
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ABSTRACT: The SAMED filter is introduced as a wide class of multi-stage filters which encompass linear FIR and nonlinear order statistic filters. The output of SAMED filter is linear combination of sub-median outputs. In this paper, optimal SAMED filter is designed for images corrupted by various noise, and performance is analogized. The experimental result shows that the efficiency of each order of SAMED filters is depends on type of noise. It is shown that low order filters are effective in Gaussian environments but high order filters are effective in impulsive case. This result may be used to follow-up research on successive SAMED filters.The Journal of the Korea institute of electronic communication sciences. 01/2012; 7(6).