Journal
IEEE TRANSACTIONS ON IMAGE PROCESSING
Volume 21, Issue 11, Pages 4544-4556Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2012.2208977
Keywords
Image super-resolution; non-local means; regularization prior; self-similarity; steering kernel regression
Funding
- National Basic Research Program of China 973 Program [2012CB316400]
- National Natural Science Foundation of China [61125106, 91120302, 61125204, 61172146, 61072093]
- Fundamental Research Funds for the Central Universities
- Ph.D. Programs Foundation of Ministry of Education of China [20090203110002]
- Natural Science Basic Research Plan in Shaanxi Province of China [2009JM8004]
- State Administration of STIND [B1320110042]
- Australian ARC [ARC DP-120103730]
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Image super-resolution (SR) reconstruction is essentially an ill-posed problem, so it is important to design an effective prior. For this purpose, we propose a novel image SR method by learning both non-local and local regularization priors from a given low-resolution image. The non-local prior takes advantage of the redundancy of similar patches in natural images, while the local prior assumes that a target pixel can be estimated by a weighted average of its neighbors. Based on the above considerations, we utilize the non-local means filter to learn a non-local prior and the steering kernel regression to learn a local prior. By assembling the two complementary regularization terms, we propose a maximum a posteriori probability framework for SR recovery. Thorough experimental results suggest that the proposed SR method can reconstruct higher quality results both quantitatively and perceptually.
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