Feature Extraction for Patch Matching in Patch-based Denoising Methods

Guangyi Chen, Adam Krzyzak

Abstract

Patch-based image denoising is a popular topic in recent years. In existing works, the distance between two patches was calculated as their Euclidian distance. When the noise level is high, this approach may not be desirable in image denoising. In this paper, we propose to extract noise-robust feature vectors from image patches and match the image patches by their Euclidian distance of the feature vectors for grey scale image denoising. Our modification takes advantage of the fact that the mean of the Gaussian white noise is zero. For every patch in the noisy image, we use lines to divide the patch into two regions with equal area and we take the mean of the right region for each line. Hence, a number of features can be extracted. We use these extracted features to match the patches in the noisy image. By introducing feature-based patch matching, our method performs favourably for highly noisy images.

Keywords
Additive Gaussian white noise; image denoising; patch-based image denoising

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DOI: 10.5566/ias.2812

Copyright (c) 2022 Guangyi Chen, Adam Krzyzak

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This work is licensed under a Creative Commons Attribution 4.0 International License.

Image Analysis & Stereology
EISSN 1854-5165 (Electronic version)
ISSN 1580-3139 (Printed version)