Image Denoising using Patch based Processing with Fuzzy Gaussian Membership Function

Abstract

paper image denoising scheme based on fuzzy Gaussian membership function. For a given corrupted image at first converted to the fuzzy values using the fuzzification method. Then extract all patches with overlaps, after extracting all patches each patch is to be permuted and apply the fuzzy Gaussian membership function. Extraction means all patches with overlaps, refer to these as coordinates in high-dimensional space, and arrange them such that they are chained in the shortest possible path. The obtained ordering, applying the fuzzy defuzzification method to convert fuzzy values to the crisp values to what should be a normal signal. This enables us to get high-quality recovery of the clean image by applying relatively simple one-dimensional smoothing operations to the reordered set of pixels. The performance of this approach is experimentally verified on a diversity of images and noise levels. The results presented here demonstrate that proposed technique is on similarity or more than the existing state of the art, in terms of both peak signal-to-noise ratio and subjective visual quality. Keywords-based processing, fuzzification, defuzzification, gaussian membership function, traveling salesman, pixel permutation, denoising.