ZHAO Shengrong, LYU Zehua, LIANG Hu, et al., “A Mixed Non-local Prior Model for Image Super-resolution Reconstruction,” Chinese Journal of Electronics, vol. 26, no. 4, pp. 778-783, 2017, doi: 10.1049/cje.2016.06.024
Citation: ZHAO Shengrong, LYU Zehua, LIANG Hu, et al., “A Mixed Non-local Prior Model for Image Super-resolution Reconstruction,” Chinese Journal of Electronics, vol. 26, no. 4, pp. 778-783, 2017, doi: 10.1049/cje.2016.06.024

A Mixed Non-local Prior Model for Image Super-resolution Reconstruction

doi: 10.1049/cje.2016.06.024
Funds:  This work is supported by the Hubei Province Natural Science Foundation (No.2013CFB152), and partly supported by the Ph.D. Programs Foundation of Ministry of Education of China (No.20120142120110).
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  • Corresponding author: LYU Zehua (corresponding author) was born in Yichang, Hubei Province, China. He received the Ph.D. degree in the School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China. He is now a lecturer in the School of Software Engineering, Huazhong University of Science and Technology, Wuhan, China. His research interests include Image Processing and Artificial Intelligence. (Email: lvzehua@hust.edu.cn)
  • Received Date: 2015-06-24
  • Rev Recd Date: 2015-09-25
  • Publish Date: 2017-07-10
  • Generating high-resolution image from a set of degraded low-resolution images is a challenge problem in image processing. Due to the ill-posed nature of Super-resolution (SR), it is necessary to find an effective image prior model to make it well-posed. For this purpose, we propose a mixed non-local prior model by adaptively combining the non-local total variation and non-local H1 models, and establish a multi-frame SR method based on this mixed non-local prior model. The unknown Highresolution (HR) image, motion parameters and hyperparameters related to the new prior model and noise statistics are determined automatically, resulting in an unsupervised super-resolution method. Extensive experiments demonstrate the effectiveness of the proposed SR method, which can not only preserve image details better but also suppress noise better.
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