Abstract
The task of single image super resolution (SR) is to generate a plausible high-resolution (HR) image from a given low-resolution (LR) measurement. This paper presents a reconstruction-based single image SR method. Firstly, an adaptive-shape non-local means (AS-NLM) model is proposed by taking the local structures around pixels into consideration. Afterwards, AS-NLM is utilized to further improve the existing non-local steering kernel regression (NLSKR) model, achieving a new model called I-NLSKR. To obtain superior performance, AS-NLM and I-NLSKR are combined, leading to a new SR algorithm named SRLNP (SR using local and non-local priors). Experimental results demonstrate that SRLNP outperforms many existing methods in both objective and subjective evaluations.
This work was supported in part by Natural Science Foundation (NSF) of China under Grants 61761005 and 61761007, and in part by the NSF of Guangxi under Grants 2016GXNSFAA380154 and 2016GXNSFAA380216.
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Li, T., Chang, K., Mo, C., Zhang, X., Qin, T. (2018). Single Image Super Resolution Using Local and Non-local Priors. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11165. Springer, Cham. https://doi.org/10.1007/978-3-030-00767-6_25
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