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

arXiv: https://arxiv.org/abs/2107.04865 · HTML: https://mlsanity.com/paper/2107.04865/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2021.

## Built on by (most cited first, 1 of 1)

- [Details Preserving Deep Collaborative Filtering-Based Method for Image Denoising](https://mlsanity.com/paper/2107.05115/) (2021)

## Abstract (arXiv)

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.