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 paper, we propose a method for real-time high density impulse noise suppression from images. In our method, we first apply an impulse detector to identify the corrupted pixels and then employ an innovative weighted-average filter to restore them. The filter takes the nearest neighboring interpolated image as the initial image and computes the weights according to the relative positions of the corrupted and uncorrupted pixels. Experimental results show that the proposed method outperforms the best existing methods in both PSNR measure and visual quality and is quite suitable for real-time applications.IEEE Signal Processing Letters 07/2014; 22(8). DOI:10.1109/LSP.2014.2381649 · 1.64 Impact Factor
Conference Paper: A Non-Iterative Adaptive Median Filter for Image Denoising[Show abstract] [Hide abstract]
ABSTRACT: In this paper, a non-iterative adaptive median filter is proposed for denoising images contaminated with impulse noise. The proposed denoising scheme operates in two steps. Firstly, the pixels are segregated as 'noisy' and 'noise-free' so that the subsequent processing can be carried out only for the noisy pixels only in the next step. Secondly, the identified noisy pixels are replaced by the median value or by its neighboring pixel value. The term 'adaptive' justifies the filters' capability to increase the size of the spatial window, depending upon the decisions made based on statistical parameters (estimated within the local window). Further, the 'non-iterative' feature projects that there is no need of recursive filtering to reduce the residual noise content. The proposed denoising method is tested on images with different characteristics and is found to produce better results in terms of the qualitative and quantitative measures of the image in comparison to other filtering approaches.2014 International Conference on Signal Processing and Integrated Networks (SPIN), Noida (UP), India; 02/2014
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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. DOI:10.1016/j.scient.2012.07.016 · 0.84 Impact Factor