An adaptive switching filter based on approximated variance for detection of impulse noise from color images

Pritamdas et al. SpringerPlus (2016) 5:1969 DOI 10.1186/s40064-016-3644-9 Open Access RESEARCH An adaptive switching filter based on approximated variance for detection of impulse noise from color images K. Pritamdas1* , Kh. Manglem Singh2 and L. Lolitkumar Singh3 *Correspondence: das1948@ gmail.com 1 Electronics and Communication Engineering (ECE), NIT Manipur, Takyelpat, Imphal 795001, India Full list of author information is available at the end of the article Abstract A new adaptive switching algorithm is presented where two adaptive filters are switched correspondingly for lower and higher noise ratio of the image. An adaptive center weighted vector median filter is used for the lower noise ratio whereas for higher noise ratio the noisy pixels are detected based on the comparison of the difference between the mean of the vector pixels in the window and the approximated variance of the vector pixels in the window. Then the window comprising the detected noisy pixel is further considered where the pixels are given exponential weights according to their similarity to the other neighboring pixels, spatially and radio metrically. The noisy pixels are then replaced by the weighted average of the pixels within the window. The filter is able to preserve higher signal content in the higher noise ratio as compared to other robust filters in comparison. With a little high in computational complexity, this technique performs well both in lower and higher noise ratios. Simulation results on various RGB images show that the proposed algorithm outperforms many other existing nonlinear filters in terms of preservation of edges and fine details. Keywords: Cumulative, Exponential, Impulse noise, Variance, Vector Background Filtering is one of the most essential steps in the applications of image processing. An image must contain the required data to show the correct information before it is used for any image processing application. But images are usually corrupted with unwanted information that causes hindrance to an efficient image processing operations. These unwanted information which are termed as noise must be removed properly from the image as a preprocessing step. Additive random noise (Gaussian noise) and salt and pepper noise are some of the most common noises found in digital image. Impulse noise which may be fixed valued noise (FVN) or random valued noise (RVN) is one of the most naturally occurring noises in digital images and it is induced in the image during image acquisition by faulty sensors or during transmission through communication channels. Noise removal techniques depends on the type of noises degrading the image and also largely on the percentage of noise corrupting the image. A number of robust filters have been proposed in literature for filtering the color images corrupted © The Author(s) 2016. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Pritamdas et al. SpringerPlus (2016) 5:1969 with impulse noise. Non-linear filters which actually work in spatial domain suit well for impulse noise removal from color images (Celebi et al. 2007). Initial approach like the marginal median filter (MMF) treats the color image channel wise in a scalar form which often leads to color artifacts (Pitas 1990). The nonlinear filters like the vector median filter (VMF) (Astola et al. 1990) and the basic vector directional filter (BVDF) (Trahanias and Venetsanopoulos 1992) which consider the color pixels as vectors and work on the concept of order-static filters, are very efficient for the impulse noise removal of color images. The VMF forms a sorted array of the cumulative distance of intensity value of the vector pixels from the surrounding pixels in the window, and then the corresponding vector pixel which gives the least value of cumulative distance in the sorted array is substituted as the vector median instead of the center pixel. And in case of VDF, the sorted array is of the cumulative angular distance of the vector pixels from the surrounding vectors in the window. Thus the output of the VDF is the vector pixel that corresponds to the least value of cumulative angular distance. The directional-distance filter (DDF) (Karakos and Trahanias 1995) combines the μ magnitude part from the VMF and the (1 − μ) angular part from the VDF in calculating the cumulative distances from a vector pixel to the other in the filtering window. The center weighted VMF (CWVMF) (Smolka et al. 2012a), center weighted VDF (CWVDF) and the center weighted DDF (CWDDF) highlight the center pixel by assigning more weight. These filters have a tendency to preserve the center pixel in the filtering window which reduces the efficiency in higher noise ratio. These are the popular filters where the filtering is done uniformly across the pixels without using an actual noise detection algorithm. These filters tend to modify the uncorrupted pixels which result in blurring of the edges and loss of fine details of the image. To overcome this particular issue noise detection schemes are introduced in the rank order static filters, that check whether the center pixel is noisy or not. Then the noisy pixel is replaced by the output of a vector filter otherwise it is left unaltered. The adaptive CWVMF (ACWVMF) (Lukac and Smolka 2003), adaptive CWVDF (ACWVDF) (Lukac 2004) and adaptive CWDDF (ACWDDF) replace the center pixel by the output of VMF, VDF and DDF respectively, if the difference between the center pixel and the corresponding center-weighted vector median is greater than a user specified threshold. A weight in the range of 0–1 is given to cumulative distance of the center pixel, for getting the vector median of the modified CWVMF (MCWVMF). In the peer group filter (PGF) the pixels in the window is sorted according to their dif√  n + 1 2 is selected from ferences from the center pixel, then a peer group of m = the sorted array, where n is the number of vector pixels in the window. If the difference between any two pixels from the peer group is greater than user-specified threshold, then the center pixel is replaced with the output of VMF. And if the difference of the individual pixel, in the peer group m, from the center pixel is less than a user specified threshold, then the center pixel is replaced with the VMF, which results in the fast PGF (FPGF) (Kenny et al. 2001; Smolka and Chydzinski 2005; Malinski and Smolka 2015). The adaptive VMF (AVMF) and adaptive VDF (AVDF) replace the center pixel with the output of VMF and VDF respectively if their respective cumulative distance is greater than a user specified threshold ∂ and T (Lukac 2002, 2003). Page 2 (...truncated)