Sparsity-based Image Denoising via Dictionary Learning and Structural Clustering

Where does the sparsity in image signals come from? Local and nonlocal image models have supplied complementary views toward the regularity in natural images the former attempts to construct or learn a dictionary of basis functions that promotes the sparsity; while the latter connects the sparsity with the self-similarity of the image source by clustering. In this paper, we present a variational framework for unifying the above two views and propose a new denoising algorithm built upon clusteringbased sparse representation (CSR). Inspired by the success of l1-optimization, we have formulated a double-header l1-optimization problem where the regularization involves both dictionary learning and structural structuring. A surrogate-function based iterative shrinkage solution has been developed to solve the double-header l1-optimization problem and a probabilistic interpretation of CSR model is also included. Our experimental results have shown convincing improvements over state-of-the-art...

• Type: Journal
• Year: 2011
• Where: CVPR
• Authors: Weisheng Dong, Xin Li
• Keywords: Computer Vision, CVPR 2011, Double-header L1-optimization Problem, Images, Natural Images