An Efficient Image Denoising Method Integrating Multi-resolution Local Clustering and Adaptive Smoothing

TL;DR

This paper introduces an efficient image denoising technique that combines multi-resolution local clustering with adaptive smoothing, outperforming existing methods in preserving details and reducing noise for various applications.

Contribution

The paper presents a novel image denoising method that integrates adaptive local clustering with multi-resolution smoothing, enhancing detail preservation and noise reduction.

Findings

Outperforms existing denoising techniques in numerical studies.

Better preserves fine image details compared to traditional methods.

Demonstrates potential for diverse modern applications.

Abstract

The importance of developing efficient image denoising methods is immense especially for modern applications such as image comparisons, image monitoring, medical image diagnostics, and so forth. Available methods in the vast literature on image denoising can address certain issues in image denoising, but no one single method can solve all such issues. For example, jump regression based methods can preserve linear edges well, but cannot preserve many other fine details of an image. On the other hand, local clustering based methods can preserve fine edge structures, but cannot perform well in presence of heavy noise. The proposed method uses various shapes and sizes of local neighborhood based on local information, and integrates this adaptive approach with the local clustering based smoothing. Theoretical justifications and numerical studies show that the proposed method indeed performs better than these two individual methods and outperforms many other state-of-the-art techniques as well. Such performance demonstrates vast potential of the applicability of the proposed method in many modern-day applications.