Single image super-resolution using hybrid patch search and local self-similarity (original) (raw)

2017 IEEE International Symposium on Circuits and Systems (ISCAS), 2017

Abstract

In this paper, we proposed a hybrid patch search process, which combines the gradient and low frequency (LF)-based patch search to further enhance the effects of the above mentioned methods. We use the assumption of local self-similarity to limit the search area within a small window, while obtaining similar results in most cases. In the proposed framework, two different patch search methods are applied. For edge regions, we use the gradient-based patch search, whereas in smooth regions, LF-based patch search is adopted. When the difference is close between two patches of the hybrid patch search, we further compare the gradient direction for verification. In the experimental results, compared with the SR method that only use LF-based patch search and the SR method that gradient-based patch search only, our proposed method gains higher PSNR and SSIM average values. Also, the computation for high frequency (HF) reconstruction is reduced by about half compared with the gradient-based SR method.

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