非局部低秩正则化视频压缩感知重构
作者:
作者单位:

上海大学

作者简介:

通讯作者:

中图分类号:

TN911.73

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)


Video Compressive Sensing Reconstruction via Nonlocal Low-Rank Regularization
Author:
Affiliation:

Shanghai University

Fund Project:

The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    摘要:

    视频压缩感知在采样资源受限的视频采集领域具有重要研究意义,重构算法是视频压缩感知系统的关键技术.为了更好地从压缩采样数据中重构视频信号,本文提出一种基于全变分与非局部低秩正则化的视频重构算法,为视频重构提供一种新的思路.该算法包括两个步骤:第一步考虑视频帧内局部光滑特性和帧间相关性,应用全变分模型作为先验约束得到初步恢复的视频帧.第二步考虑视频帧内和帧间的非局部自相似性,应用改进的非局部低秩正则化算法对其进一步重构,该步骤对初步恢复的图像帧分块,在本帧和关键帧中寻找相似块,构建低秩矩阵进行低秩正则化重构.仿真结果表明,提出的算法能够精确重构视频信号,相比主流的视频压缩感知重构算法具有更高的重构质量.

    Abstract:

    Compressive video sensing (CVS) has great research significance in the video acquisition system with limited sampling resources. In this paper, we proposed a reconstruction algorithm based on total variation (TV) and nonlocal low-rank regularization (NLR-CS) to better reconstruct video signal from compressive sampled data. This algorithm consists two steps: The first step considers local correlation between and within video frames, applies TV as the prior constraint to obtain the initial recovered frame; In the second step, the improved NLR-CS algorithm is utilized to further reconstruct video frame considering the nonlocal self-similarity (NLSS). This step first blocks the initial recovered frame, finds similar blocks in the current frame and the key frames to construct low-rank matrix, then a low-ranking regularization reconstruction is performed. Experimental results show that the proposed algorithm can reconstruct video signals well, obtains higher video reconstruction accuracy than other CVS reconstruction algorithms.

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  • 收稿日期:2020-03-19
  • 最后修改日期:2020-07-22
  • 录用日期:2020-08-28
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