Collaborative Filtering-Based Method for Low-Resolution and Details Preserving Image Denoising

TL;DR

This paper introduces a collaborative filtering-based denoising algorithm that effectively preserves image details and performs well on low-resolution images, addressing limitations of existing methods that often smooth out details or distort low-res images.

Contribution

The proposed CoFiB algorithm utilizes weighted sparse domain collaborative denoising to better preserve details and handle low-resolution images, a novel approach in image denoising.

Findings

Preserves image details effectively.

Performs well on low-resolution images.

Outperforms existing denoising algorithms.

Abstract

Over the years, progressive improvements in denoising performance have been achieved by several image denoising algorithms that have been proposed. Despite this, many of these state-of-the-art algorithms tend to smooth out the denoised image resulting in the loss of some image details after denoising. Many also distort images of lower resolution resulting in a partial or complete structural loss. In this paper, we address these shortcomings by proposing a collaborative filtering-based (CoFiB) denoising algorithm. Our proposed algorithm performs weighted sparse domain collaborative denoising by taking advantage of the fact that similar patches tend to have similar sparse representations in the sparse domain. This gives our algorithm the intelligence to strike a balance between image detail preservation and noise removal. Our extensive experiments showed that our proposed CoFiB algorithm does not only preserve the image details but also perform excellently for images of any given resolution where many denoising algorithms tend to struggle, specifically at low resolutions.