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Image denoising is a classical signal processing problem that has receivedsignificant interest within the image processing community during the past twodecades. Most of the algorithms for image denoising has focused on the paradigmof non-local similarity, where image blocks in the neighborhood that aresimilar, are collected to build a basis for reconstruction. Through rigorousexperimentation, this paper reviews multiple aspects of image denoisingalgorithm development based on non-local similarity. Firstly, the concept ofnon-local similarity as a foundational quality that exists in natural imageshas not received adequate attention. Secondly, the image denoising algorithmsthat are developed are a combination of multiple building blocks, makingcomparison among them a tedious task. Finally, most of the work surroundingimage denoising presents performance results based on Peak-Signal-to-NoiseRatio (PSNR) between a denoised image and a reference image (which is perturbedwith Additive White Gaussian Noise). This paper starts with a statisticalanalysis on non-local similarity and its effectiveness under various noiselevels, followed by a theoretical comparison of different state-of-the-artimage denoising algorithms. Finally, we argue for a methodological overhaul toincorporate no-reference image quality measures and unprocessed images (raw)during performance evaluation of image denoising algorithms.

A Critical Analysis of Patch Similarity Based Image Denoising Algorithms