NDLI: Sparsity-based image denoising via dictionary learning and structural clustering

Content Provider	IEEE Xplore Digital Library
Author	Weisheng Dong Xin Li Lei Zhang Guangming Shi
Copyright Year	2011
Description	Author affiliation: HK Polytech. Univ. (Lei Zhang) \|\| WVU (Xin Li) \|\| Xidian University (Weisheng Dong; Guangming Shi)
Abstract	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 clustering-based 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 denoising technique BM3D on the class of regular texture images. The PSNR performance of CSR denoising is at least comparable and often superior to other competing schemes including BM3D on a collection of 12 generic natural images.
Starting Page	457
Ending Page	464
File Size	785157
Page Count	8
File Format	PDF
ISBN	9781457703942
ISSN	10636919
e-ISBN	9781457703959
DOI	10.1109/CVPR.2011.5995478
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Dictionaries Noise reduction PSNR Clustering algorithms Image denoising Manifolds Optimization
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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