Self-synthesis with sparse prior for image interpolation

Kai Guo; Xiaokang Yang; Weiyao Lin; Rui Zhang; Songyu Yu

doi:10.1117/1.3572137

Abstract

Image interpolation addresses the problem of obtaining high resolution (HR) images from its low resolution (LR) counterparts. For observed LR images with aliasing artifacts caused by undersampling, commonly used interpolation methods cannot recover HR images well, and may often interpolate over-fitting artifacts. In this paper, based on the observation that natural images normally have redundant similar patches, a new patch-synthesis-based interpolation method is proposed for image interpolation. In the proposed method, an inference method based on Markov chain is adopted to select the best patches from the input LR image and synthesize them into the undersampled areas of a desired HR image. In order to improve the efficiency of the algorithm, we also introduce fields of experts to model the sparse prior knowledge and use it to measure the compatibilities among neighboring patches. Experimental results compared with traditional interpolation methods demonstrate that our method cannot only alleviate the aliasing artifact, but also produce better results in terms of quantitative evaluation and subjective visual quality

Citation Download Citation

Kai Guo, Kai Guo, Xiaokang Yang, Xiaokang Yang, Weiyao Lin, Weiyao Lin, Rui Zhang, Rui Zhang, Songyu Yu, Songyu Yu, } "Self-synthesis with sparse prior for image interpolation," Optical Engineering 50(5), 057002 (1 May 2011). https://doi.org/10.1117/1.3572137 . Submission:

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