Study of Color Image Denoising Filters

Abstract

Image Denoising is an essential pre-processing task before the image is further processed by segmentation, feature extraction, texture analysis etc. Denoising is employed to evacuate the noise while retaining the sharp edges and other texture details of the image however much as could reasonably be expected. This noise gets present amid acquisition, transmission, and storage processes. Visual quality of the image is degraded due to the noise introduced in it. The noise considered in this thesis is additive white Gaussian noise (AWGN). Some spatial-Domain filters like Mean filter, Median filter, Weighted median filter, Wiener filter etc. have been studied in this work for suppression of AWGN. The recently developed Block matching and 3D filtering approach have also been performed efficiently under high variance of noise . Performance of these filters are compared in terms of peak-signal-to-noise-ratio (PSNR), structural similarity index (SSIM). Results of ten different standard color images have been compared under varied noise levels. The Mean filter for Gaussian noise removal under low noise conditions works efficiently. Median filter , weighted median filter and Wiener filter performs better than mean filter.
BM3D is a state of the art technique, which gives better performance than all the other techniques studied here. All the studied filters are applied on the color images. As BM3D outperforms all of the techniques studied here, our main focus is BM3D. BM3D is a transform domain filtering method which exploit the high correlation between the similar blocks in a natural image. All similar image blocks are collected in group in this method, and then denoising is done in a 3D transform domain. Denoising is done by hard thresholding and Wiener shrinkage. BM3D is applied for color image denoising after converting the image from RGB color space to YUV color space so that the edge details of the image can be extracted, then the filtering is applied on the noisy image.