Journal
IEEE SIGNAL PROCESSING LETTERS
Volume 22, Issue 3, Pages 336-340Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSP.2014.2360038
Keywords
Kernel principal analysis (KPCA); pre-image; regression; super-resolution (SR)
Categories
Funding
- National Natural Science Foundation of China [61271393, 61301183]
- China Postdoctoral Science Foundation [2013M540947, 2014T70083]
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In this letter, we propose a novel approach for single-image super-resolution (SR). Our method is based on the idea of learning a dictionary which can capture the high-order statistics of high-resolution (HR) images. It is of central importance in image SR application, since the high-order statistics play a significant role in the reconstruction of HR image structure. Kernel principal component analysis (KPCA) is adopted to learn such a dictionary. A compact solution is adopted to reduce the time complexity of learning and testing for KPCA. Meanwhile, kernel ridge regression is employed to connect the input low-resolution (LR) image patches with the HR coding coefficients. Experimental results show that the proposed method is effective and efficient in comparison with state-of-art algorithms.
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