Reconstruction Algorithm for Compressed Sensing of Video Based on Joint Total Variation Minimization

CHANG Kan; QIN Tuan-fa; TANG Zhen-hua

doi:10.3969/j.issn.0372-2112.2014.12.012

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Reconstruction Algorithm for Compressed Sensing of Video Based on Joint Total Variation Minimization

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- Reconstruction Algorithm for Compressed Sensing of Video Based on Joint Total Variation Minimization
- Acta Electronica Sinica Vol. 42, Issue 12, Pages: 2415-2421(2014)
- 作者机构：
  
  广西大学计算机与电子信息学院,广西,南宁,530004
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2014.12.012
  CLC： TN919.8
- Published：2014
- 稿件说明：
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CHANG Kan, QIN Tuan-fa, TANG Zhen-hua. Reconstruction Algorithm for Compressed Sensing of Video Based on Joint Total Variation Minimization[J]. Acta Electronica Sinica, 2014, 42(12): 2415-2421.
DOI：

CHANG Kan, QIN Tuan-fa, TANG Zhen-hua. Reconstruction Algorithm for Compressed Sensing of Video Based on Joint Total Variation Minimization[J]. Acta Electronica Sinica, 2014, 42(12): 2415-2421. DOI： 10.3969/j.issn.0372-2112.2014.12.012.

摘要

在接收端进行预测和补偿的残差重建算法是一种高效的视频压缩感知重建算法.但是

残差重建算法没有应用当前图像的稀疏先验

算法性能完全依赖于预测结果的准确性.针对此问题

本文提出了一种基于联合总变分最小化的视频压缩感知重建算法以提升重建图像质量.为了联合应用待重建图像及对应残差值的稀疏先验

在所建立的重建模型中

分别计算目标图像块及其残差值的总变分范数;为求解最小化问题

引入新的变量

并基于split Bregman方法设计了一种迭代求解算法.实验结果表明

与同类算法相比

提出的重建算法可以在相同采样率下获得更高质量的重建图像.

Abstract

The residual reconstruction algorithm

which performs prediction and compensation at the receiver side

is an efficient reconstruction algorithm for compressed sensing of video.However

the residual reconstruction algorithm doesn't make use of the sparsity prior of an image

and the performance of the algorithm all relies on the accuracy of prediction.This paper proposes a reconstruction algorithm based on joint total variation (TV) minimization to improve the quality of reconstructed images.In order to jointly exploit the sparsity of images and their residual

TV norm of a target image block and TV norm of its residual are both calculated in the established reconstruction model.To solve the minimization problem

new variables are introduced

and an iterative algorithm is developed based on the split Bregman method.The experimental results show that when compared with other traditional algorithms

the proposed algorithm is able to provide higher quality of reconstructed images at the same sampling rates.

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