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

作　　者：ZHAO Shengrong LYU Zehua LIANG Hu Mudar SAREM 

机构地区：[1]School of Computer Science and Technology, Huazhong University of Science and Technology [2]School of Software Engineering, Huazhong University of Science and Technology

出　　处：《Chinese Journal of Electronics》2017年第4期778-783,共6页电子学报（英文版）

基　　金：supported by the Hubei Province Natural Science Foundation(No.2013CFB152);partly supported by the Ph.D.Programs Foundation of Ministry of Education of China(No.20120142120110)

摘　　要：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.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.

关 键 词：Super-resolution(SR) Bayesian frame work Non-local H1 Non-local total variation Non-local edge & texture preserving(NLE&TP) 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]