Texture variation adaptive image denoising with nonlocal PCA

TL;DR

This paper introduces a novel image denoising method that adaptively groups texture patches and applies a PCA-transform-domain variation adaptive filter, effectively preserving complex textures especially in stochastic regions.

Contribution

It proposes an adaptive clustering and PCA-based filtering approach that enhances texture preservation in image denoising, outperforming traditional PCA thresholding methods.

Findings

Superior texture preservation demonstrated in experiments

Effective noise robustness through adaptive clustering

Improved denoising performance on natural and raw images

Abstract

Image textures, as a kind of local variations, provide important information for human visual system. Many image textures, especially the small-scale or stochastic textures are rich in high-frequency variations, and are difficult to be preserved. Current state-of-the-art denoising algorithms typically adopt a nonlocal approach consisting of image patch grouping and group-wise denoising filtering. To achieve a better image denoising while preserving the variations in texture, we first adaptively group high correlated image patches with the same kinds of texture elements (texels) via an adaptive clustering method. This adaptive clustering method is applied in an over-clustering-and-iterative-merging approach, where its noise robustness is improved with a custom merging threshold relating to the noise level and cluster size. For texture-preserving denoising of each cluster, considering that the variations in texture are captured and wrapped in not only the between-dimension energy variations but also the within-dimension variations of PCA transform coefficients, we further propose a PCA-transform-domain variation adaptive filtering method to preserve the local variations in textures. Experiments on natural images show the superiority of the proposed transform-domain variation adaptive filtering to traditional PCA-based hard or soft threshold filtering. As a whole, the proposed denoising method achieves a favorable texture preserving performance both quantitatively and visually, especially for stochastic textures, which is further verified in camera raw image denoising.