Multiscale Hybrid Non-local Means Filtering Using Modified Similarity Measure

TL;DR

This paper introduces a multiscale non-local means filtering method with a modified similarity measure that uses normal vector patches, leading to improved image denoising performance compared to existing algorithms.

Contribution

It proposes a novel multiscale implementation of non-local means filtering combined with a modified similarity measure based on normal vectors, enhancing denoising accuracy.

Findings

Achieves comparable or superior denoising results to state-of-the-art methods.

Utilizes a two-step process involving wavelet domain filtering and modified patch comparison.

Demonstrates improved noise reduction with preserved image details.

Abstract

A new multiscale implementation of non-local means filtering for image denoising is proposed. The proposed algorithm also introduces a modification of similarity measure for patch comparison. The standard Euclidean norm is replaced by weighted Euclidean norm for patch based comparison. Assuming the patch as an oriented surface, notion of normal vector patch is being associated with each patch. The inner product of these normal vector patches is then used in weighted Euclidean distance of photometric patches as the weight factor. The algorithm involves two steps: The first step is multiscale implementation of an accelerated non-local means filtering in the stationary wavelet domain to obtain a refined version of the noisy patches for later comparison. This step is inspired by a preselection phase of finding similar patches in various non-local means approaches. The next step is to apply the modified non-local means filtering to the noisy image using the reference patches obtained in the first step. These refined patches contain less noise, and consequently the computation of normal vectors and partial derivatives is more accurate. Experimental results indicate equivalent or better performance of proposed algorithm as compared to various state of the art algorithms.