A Critical Analysis of Patch Similarity Based Image Denoising Algorithms

TL;DR

This paper critically reviews non-local similarity based image denoising algorithms, highlighting the importance of non-local similarity, comparing state-of-the-art methods, and advocating for improved evaluation metrics including no-reference quality measures.

Contribution

It provides a comprehensive analysis of non-local similarity in denoising, compares existing algorithms theoretically, and proposes a shift towards no-reference quality assessment methods.

Findings

Non-local similarity exists as a fundamental natural image property.

Current algorithms combine multiple components, complicating comparison.

PSNR-based evaluation may be insufficient for assessing denoising quality.

Abstract

Image denoising is a classical signal processing problem that has received significant interest within the image processing community during the past two decades. Most of the algorithms for image denoising has focused on the paradigm of non-local similarity, where image blocks in the neighborhood that are similar, are collected to build a basis for reconstruction. Through rigorous experimentation, this paper reviews multiple aspects of image denoising algorithm development based on non-local similarity. Firstly, the concept of non-local similarity as a foundational quality that exists in natural images has not received adequate attention. Secondly, the image denoising algorithms that are developed are a combination of multiple building blocks, making comparison among them a tedious task. Finally, most of the work surrounding image denoising presents performance results based on Peak-Signal-to-Noise Ratio (PSNR) between a denoised image and a reference image (which is perturbed with Additive White Gaussian Noise). This paper starts with a statistical analysis on non-local similarity and its effectiveness under various noise levels, followed by a theoretical comparison of different state-of-the-art image denoising algorithms. Finally, we argue for a methodological overhaul to incorporate no-reference image quality measures and unprocessed images (raw) during performance evaluation of image denoising algorithms.