基于多参考帧假设优化的压缩感知重构算法

阔永红; 王薷泉; 陈健

doi:10.11959/j.issn.1000-436x.2017297

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基于多参考帧假设优化的压缩感知重构算法

学术论文 | 更新时间：2024-06-05

- 基于多参考帧假设优化的压缩感知重构算法
- Reconstruction algorithm based on multi-reference frames hypothesis optimization for compressive sensing
- 通信学报 2017年38卷第12期页码：1-9
- 作者机构：
  
  西安电子科技大学通信工程学院，陕西西安 710071
- 作者简介：
  
  [ "阔永红（1967-），女，陕西宝鸡人，西安电子科技大学教授，主要研究方向为信号处理、认知无线电、无线传感器网络等。" ]
  [ "王薷泉（1991-），男，陕西西安人，西安电子科技大学博士生，主要研究方向为视频编解码、压缩感知等。" ]
  [ "陈健（1968-），男，江苏如东人，西安电子科技大学教授、博士生导师，主要研究方向为认知无线电、OFDM、无线传感器网络等。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61771366);高等学校引智计划基金资助项目(B08038)
- DOI：10.11959/j.issn.1000-436x.2017297
  中图分类号： TP919.8
- 网络出版日期：2017-12，
  
  纸质出版日期：2017-12-25
- 稿件说明：
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阔永红, 王薷泉, 陈健. 基于多参考帧假设优化的压缩感知重构算法[J]. 通信学报, 2017,38(12):1-9.

Yong-hong KUO, Ru-quan WANG, Jian CHEN. Reconstruction algorithm based on multi-reference frames hypothesis optimization for compressive sensing[J]. Journal on communications, 2017, 38(12): 1-9.
阔永红, 王薷泉, 陈健. 基于多参考帧假设优化的压缩感知重构算法[J]. 通信学报, 2017,38(12):1-9. DOI： 10.11959/j.issn.1000-436x.2017297.

Yong-hong KUO, Ru-quan WANG, Jian CHEN. Reconstruction algorithm based on multi-reference frames hypothesis optimization for compressive sensing[J]. Journal on communications, 2017, 38(12): 1-9. DOI： 10.11959/j.issn.1000-436x.2017297.

摘要

在多假设分布式压缩视频感知系统中，多假设的质量对重构性能意义重大。现有工作中，对于多假设集合获取的研究并未得到关注。提出一种多参考帧假设集合优化选择（MRHO）算法，增加参考帧数目以扩大假设选择范围，通过假设优化选择，在相同假设集合尺寸下提高了集合质量。仿真表明，MRHO算法有效提高了视频重构质量。

Abstract

In multi-hypothesis based distributed compressed video sensing systems

the quality of the multi-hypothesis set has important influence on the reconstruction performance of decoder.However

the acquiring of the hypothesis set has not been concerned in existing works.A reconstruction algorithm based on multi-reference frames hypothesis optimization (MRHO) was proposed.This algorithm expanded the selection of hypothesis vectors by increasing the number of reference frames.The quality of the prediction set was improved by hypotheses optimization selection under the same size with the original hypothesis set.Simulation results show that the proposed MRHO algorithm effectively improves the reconstructed quality of the distributed compressed video sensing scheme.

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references

CANDES E J , ROMBERG J , TAO T . Robust uncertainty principles:exact signal reconstruction from highly incomplete frequency information [J ] . IEEE Transactions on Information Theory , 2006 , 52 ( 2 ): 489 - 509 .

DONOHO D L . Compressed sensing [J ] . IEEE Transactions on Information Theory , 2006 , 52 ( 4 ): 1289 - 1306 .

DO T T , CHEN Y , NGUYEN D T , et al . Distributed compressed video sensing [C ] // Image Processing (ICIP),2009 16th IEEE International Conference . 2009 : 1393 - 1396 .

KANG L W , LU C S . Distributed compressive video sensing [C ] // International Conference on Acoustics,Speech and Signal Processing . 2009 : 1169 - 1172 .

ASIF M S , ROMBERG J . Low-complexity video compression and compressive sensing [J ] . Conference on signals,Systems＆Computers , 2013 , 118 ( 1 ): 579 - 583 .

芦存博 , 肖嵩 , 权磊 . 基于二进制序列族的压缩感知测量矩阵的构造 [J ] . 电子与信息学报 , 2016 , 38 ( 7 ): 1682 - 1688 .

LU C B , XIAO S , QUAN L . Construction of compressed sensing measurement matrix based on binary sequence family [J ] . Journal of Electronics＆Information Technology , 2016 , 38 ( 7 ): 1682 - 1688 .

赵瑞珍 , 王若乾 , 张凤珍 , 等 . 分块的有序范德蒙矩阵作为压缩感知测量矩阵的研究 [J ] . 电子与信息学报 , 2015 , 37 ( 6 ): 1317 - 1322 .

ZHAO R Z , WANG R Q , ZHANG F Z , et al . Research on the blocked ordered vandermonde matrix used as measurement matrix for compressed sensing [J ] . Journal of Electronics ＆ Information Technology , 2015 , 37 ( 6 ): 1317 - 1322 .

党骙 , 马林华 , 田雨 , 等 . m序列压缩感知测量矩阵构造 [J ] . 西安电子科技大学学报 , 2015 , 42 ( 2 ): 186 - 192 .

DANG K , MA L H , TIAN Y , et al . Construction of the compressive sensing measurement matrix based on m sequences [J ] . Journal of Xidian University , 2016 , 42 ( 2 ): 186 - 192 .

CHEN C , TRAMEL E W , FOWLER J E . Compressed-sensing recovery of images and video using multihypothesis predictions [C ] // The 45th Asilomar Conference . 2011 : 1193 - 1198 .

FOWLER J E , MUN S , TRAMEL E W . Block-based compressed sensing of images and video [J ] . Foundations and Trends in Signal Processing , 2012 , 4 ( 4 ): 297 - 416 .

CHEN J , CHEN Y Z , QIN D , et al . H.An elastic net-based hybrid hypothesis method for compressed video sensing [J ] . Multimedia Tools and Applications , 2015 , 74 ( 6 ): 2085 - 2108 .

KUO Y H , WU K , CHEN J . A scheme for distributed compressed video sensing based on hypothesis set optimization techniques [J ] . Multidimensional Systems and Signal Processing , 2017 , 28 ( 1 ): 129 - 148 .

陈建 , 苏凯雄 , 杨秀芝 , 等 . 基于变分模型的块压缩感知重构算法 [J ] . 通信学报 , 2016 , 37 ( 1 ): 100 - 109 .

CHEN J , SU K X , YANG X Z , et al . Reconstruction algorithm for block compressed sensing based on variation model [J ] . Journal on Communications , 2016 , 37 ( 1 ): 100 - 109 .

杨春玲 , 欧伟枫 . CVS中基于多参考帧的最优多假设预测算法 [J ] . 华南理工大学学报 , 2016 , 44 ( 1 ): 1 - 8 .

YANG C L , OU W F . Multi-reference frames-based optimal multi-hypothesis prediction algorithm for compressed video sensing [J ] . Journal of South China University of Technology , 2016 , 44 ( 1 ): 1 - 8 .

KUO Y H , WANG S T , QIN D , et al . High-quality decoding method based on resampling and re-reconstruction [J ] . Electronics Letters , 2013 , 49 ( 16 ): 991 - 992 ，

MUN S , FOWLER J E . Block compressed sensing of images using directional transforms [C ] // Image Processing (ICIP),2009 16th IEEE International Conference . IEEE,Cairo,Egypt , 2009 : 3021 - 3024 .

DO T T , CHEN Y , NGUYEN D T , et al . Distributed compressed video sensing [C ] // International Conference on Image Processing . IEEE,Cairo,Egypt , 2009 : 1393 - 1396 .

TRAMEL E W , FOWLER J E . Video compressed sensing with multihypothesis [C ] // IEEE Data Compression Conference . IEEE,Snowbird,UT,USA , 2011 : 193 - 202 .

MUN S , FOWLER J E . Residual reconstruction for block-based compressed sensing of video [C ] // Data Compression Conference (DCC) . IEEE,Snowbird,UT,USA , 2011 : 183 - 192 .

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