基于非局部均值和总变分最小化的单视频超分辨率算法  

Single Video Super-resolution Algorithm Based on Non-local Means and Total Variation Minimization

作　　者：陈诚[1] 常侃[1,2,3] 莫彩网 李天亦 覃团发[1,2,3] 

机构地区：[1]广西大学计算机与电子信息学院,南宁530004 [2]广西大学广西多媒体通信与网络技术重点实验室,南宁530004 [3]广西大学广西高校多媒体通信与信息处理重点实验室,南宁530004

出　　处：《计算机科学》2018年第3期263-267,共5页Computer Science

基　　金：国家自然科学基金项目(61761005;61401108);广西自然科学基金项目(2016GXNSFAA380154)资助

摘　　要：传统的基于重建的单视频超分辨率方法能够获得较好的重建效果。然而,已有算法没有充分利用视频内的帧间、帧内相关性,重建效果仍有待提升。针对这一问题,提出了一种新的单视频超分辨率算法。为充分利用帧内相关性,采用非局部均值模型表征帧内非局部结构特性,采用总变分模型表征帧内局部结构特性;为了探索帧间相关性,采用光流法进行帧间预测。最后,为了求解所建立的优化问题,提出了基于split-Bregman方法的快速迭代算法。实验结果表明,与同类算法相比,所提算法在主、客观质量上均有相应的提升。The traditional reconstruction-based single video super-resolution algorithms are able to solve the video super-resolution problem well.However,the existing algorithms have not fully exploited the correlation in intra-frames and inter-frames,which leaves much space for further improvement.This paper proposed a new single video super-resolution algorithm to solve this problem.When exploiting the spatial correlations,the non-local means model is used to get the non-local structural property and the total variation model is utilized to get the local structural property.In order to exploit inter-frame correlation,optical flow method is applied to perform inter-frame estimation.Finally,to solve the established optimization problem,a split-Bregman method based fast iteration algorithm was proposed.The experimental results demonstrate the effectiveness of the proposed algorithm.Compared with other algorithms,the proposed algorithm is able to achieve better subjective and objective results.

关 键 词：视频超分辨率 非局部均值 总变分 光流法 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]