Image Denoising by Gaussian Patch Mixture Model and Low Rank Patches

TL;DR

This paper introduces a novel image denoising method combining Gaussian patch mixture modeling with low rank approximation, improving patch matching accuracy and effectively handling Gaussian noise, outperforming current state-of-the-art techniques.

Contribution

It proposes a new approach that integrates local and global patch matching with a Gaussian-specific low rank model, enhancing denoising performance.

Findings

Outperforms existing methods in PSNR and SSIM

Achieves better visual quality in denoised images

Effectively handles Gaussian noise with a new low rank model

Abstract

Non-local self-similarity based low rank algorithms are the state-of-the-art methods for image denoising. In this paper, a new method is proposed by solving two issues: how to improve similar patches matching accuracy and build an appropriate low rank matrix approximation model for Gaussian noise. For the first issue, similar patches can be found locally or globally. Local patch matching is to find similar patches in a large neighborhood which can alleviate noise effect, but the number of patches may be insufficient. Global patch matching is to determine enough similar patches but the error rate of patch matching may be higher. Based on this, we first use local patch matching method to reduce noise and then use Gaussian patch mixture model to achieve global patch matching. The second issue is that there is no low rank matrix approximation model to adapt to Gaussian noise. We build a new model according to the characteristics of Gaussian noise, then prove that there is a globally optimal solution of the model. By solving the two issues, experimental results are reported to show that the proposed approach outperforms the state-of-the-art denoising methods includes several deep learning ones in both PSNR / SSIM values and visual quality.