Nonlocally Centralized Sparse Representation for Image Restoration
Top Cited Papers
- 21 December 2012
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Image Processing
- Vol. 22 (4), 1620-1630
- https://doi.org/10.1109/tip.2012.2235847
Abstract
Sparse representation models code an image patch as a linear combination of a few atoms chosen out from an over-complete dictionary, and they have shown promising results in various image restoration applications. However, due to the degradation of the observed image (e.g., noisy, blurred, and/or down-sampled), the sparse representations by conventional models may not be accurate enough for a faithful reconstruction of the original image. To improve the performance of sparse representation-based image restoration, in this paper the concept of sparse coding noise is introduced, and the goal of image restoration turns to how to suppress the sparse coding noise. To this end, we exploit the image nonlocal self-similarity to obtain good estimates of the sparse coding coefficients of the original image, and then centralize the sparse coding coefficients of the observed image to those estimates. The so-called nonlocally centralized sparse representation (NCSR) model is as simple as the standard sparse representation model, while our extensive experiments on various types of image restoration problems, including denoising, deblurring and super-resolution, validate the generality and state-of-the-art performance of the proposed NCSR algorithm.Keywords
This publication has 38 references indexed in Scilit:
- From learning models of natural image patches to whole image restorationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2011
- FSIM: A Feature Similarity Index for Image Quality AssessmentIEEE Transactions on Image Processing, 2011
- Image restoration through l0 analysis-based sparse optimization in tight framesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2009
- Super-resolution from a single imagePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2009
- From Local Kernel to Nonlocal Multiple-Model Image DenoisingInternational Journal of Computer Vision, 2009
- Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring ProblemsIEEE Transactions on Image Processing, 2009
- Enhancing Sparsity by Reweighted ℓ 1 MinimizationJournal of Fourier Analysis and Applications, 2008
- Removing camera shake from a single photographACM Transactions on Graphics, 2006
- A Review of Image Denoising Algorithms, with a New OneMultiscale Modeling & Simulation, 2005
- Motion Analysis for Image Enhancement: Resolution, Occlusion, and TransparencyJournal of Visual Communication and Image Representation, 1993