Correlation Preserving Sparse Coding Over Multi-level Dictionaries for Image Denoising

TL;DR

This paper introduces a novel image denoising approach using correlation preserving sparse coding with graph-based and locality-constrained regularizers, leading to improved performance in preserving image details and reducing noise.

Contribution

It proposes a new correlation preserving sparse coding framework with regularizers that enhance global and local structure preservation in image denoising.

Findings

Achieves state-of-the-art PSNR in denoising tasks

Improves subjective visual quality of denoised images

Effectively preserves textures and structures in noisy images

Abstract

In this letter, we propose a novel image denoising method based on correlation preserving sparse coding. Because the instable and unreliable correlations among basis set can limit the performance of the dictionary-driven denoising methods, two effective regularized strategies are employed in the coding process. Specifically, a graph-based regularizer is built for preserving the global similarity correlations, which can adaptively capture both the geometrical structures and discriminative features of textured patches. In particular, edge weights in the graph are obtained by seeking a nonnegative low-rank construction. Besides, a robust locality-constrained coding can automatically preserve not only spatial neighborhood information but also internal consistency present in noisy patches while learning overcomplete dictionary. Experimental results demonstrate that our proposed method achieves state-of-the-art denoising performance in terms of both PSNR and subjective visual quality.