Optimization of Clustering for Clustering-based Image Denoising

TL;DR

This paper introduces a novel clustering-based image denoising method that leverages global clustering of image blocks and dictionary learning, outperforming traditional methods like K-SVD in reducing additive white Gaussian noise.

Contribution

The paper proposes a new clustering approach for image denoising that addresses data selection and clustering method suitability, enhancing dictionary learning effectiveness.

Findings

Outperforms traditional dictionary learning methods like K-SVD.

Effective in reducing additive white Gaussian noise in images.

Utilizes global clustering of image blocks for improved denoising.

Abstract

In this paper, the problem of de-noising of an image contaminated with additive white Gaussian noise (AWGN) is studied. This subject has been continued to be an open problem in signal processing for more than 50 years. In the present paper, we suggest a method based on global clustering of image constructing blocks. Noting that the type of clustering plays an important role in clustering-based de-noising methods, we address two questions about the clustering. First, which parts of data should be considered for clustering? Second, what data clustering method is suitable for de-noising? Clustering is exploited to learn an over complete dictionary. By obtaining sparse decomposition of the noisy image blocks in terms of the dictionary atoms, the de-noised version is achieved. Experimental results show that our dictionary learning framework outperforms traditional dictionary learning methods such as K-SVD.