Robust Low‐Rank Kernel Dictionary Learning for Enhancing Noisy Images

ABSTRACT Denoising is a key preprocessing to enhance contaminated images. There have been lots of literature tackling such an issue. Most of them focus on pixel‐level processing and ignore intrinsic sparsity and low‐rank properties of noise and objects, respectively, in images. To argue this issue, a novel image enhancement model is proposed to eliminate or suppress noise, which integrates robust principal component analysis with kernel dictionary learning (KDL). Specifically, the Kernel trick is introduced to nonlinearly map image data and low‐rank dictionary to be learned to high‐dimensional spaces so as to separate sparsity noise from contaminated images. The proposed model is abbreviate as RSKDL. In RSKDL, the intrinsic structural characteristic of images is unclosed by adaptively learning the affinity graph of image data so as to ensure the enhanced images inheriting the manifold structure of original images. Meantime, the non‐convex sparsity regularization on the residual between original images and enhanced ones is imposed to exclude noise from original data. Extensive experiments on several image datasets show that the proposed model outperforms existing methods for image denoising.