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计算机工程

• 图形图像处理 • 上一篇    下一篇

结合稀疏编码模型的多帧图像超分辨率重建

卢 健,孙 怡   

  1. (大连理工大学信息与通信工程学院,辽宁大连116024)
  • 收稿日期:2014-05-06 出版日期:2015-05-15 发布日期:2015-05-15
  • 作者简介:卢 健(1978 - ),男,博士研究生,主研方向:图像处理;孙 怡,教授。

Multi-frame Image Super-resolution Reconstruction Combined with Sparse Coding Model

LU Jian,SUN Yi   

  1. (School of Information and Communication Engineering,Dalian University of Technology,Dalian 116024,China)
  • Received:2014-05-06 Online:2015-05-15 Published:2015-05-15

摘要: 传统序列超分辨率方法对低分辨率视频序列的要求较高,一旦序列中没有包含足够的信息,会造成重建高 分辨率图像质量的下降。为此,提出一种结合稀疏编码模型的序列超分辨率算法。利用概率运动场从低分辨率序 列中重建一幅高分辨率图像,根据自适应阈值确定重建有效和无效区域,使用稀疏编码模型对无效区域进行补全 重建。实验结果表明,该算法可以采用序列自身的信息和稀疏字典中的信息来重建高分辨率图像,在序列信息有 破缺时,与仅利用序列自身信息或仅利用单幅图像的算法相比,具有更好的鲁棒性和广泛的适用性。

关键词: 超分辨率, 稀疏编码, 图像补全, 非局部正则化, 线性反问题

Abstract: Classic multi-frame Super-resolution(SR) techniques strongly rely on the supportability of Low-resolution (LR) frames. When the frames contain insufficient information,annoying artifacts often appear in the SR outcome. To solve this problem,a multi-frame SR combined with sparse coding technique is proposed in this paper. A high-resolution frame is reconstructed by the help of probabilistic motion estimation,and meanwhile effective / ineffective regions can also be determined by using an adaptive threshold segment. A sparse-coding-based completion technique is applied to recover the ineffective regions. Experimental results show that the proposed algorithm can essentially exploit the information from both LR frames and sparse coding dictionary. Compared with SR methods which depend only on image sequence itself or a single frame,the proposed algorithm has better robustness and extensive applicability.

Key words: Super-resolution(SR), sparse coding, image completion, non-local regularization, linear inverse problem

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