Computer Science ›› 2026, Vol. 53 ›› Issue (7): 54-61.doi: 10.11896/jsjkx.250400109

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Dual-view Separation and Reconstruction Method from Fused Random Compressed Measurement

HU Tao, CHEN Zan, FENG Yuanjing   

  1. College of Information Engineering,Zhejiang University of Technology,Hangzhou 310014,China
  • Received:2025-04-23 Revised:2025-07-19 Online:2026-07-15 Published:2026-07-10
  • About author:HU Tao,born in 1999,postgraduate.His main research interests include image compressed sensing and deep learning.
    CHEN Zan,born in 1989,Ph.D,postgraduate supervisor.His main research interests include compressed sensing,sparse coding and image robust coding transmission.
  • Supported by:
    National Natural Science Foundation of China(U22A2040,U23A20334), Zhejiang Province “High-level Talent Special Support Program” Scientific and Technological Innovation(2021R52004) and Zhejiang Province Leading Geese Plan(2024C03093).

Abstract: Compressed sensing(CS) technology has brought revolutionary advancements in image acquisition and reconstruction.However,research on multi-view CS is still in its early stages,and a unified optimization model for multi-view compressed reconstruction under single-sensor,single-measurement conditions has not yet been established.This paper proposes a CS framework tailored for dual-view scenarios,which effectively separates and reconstructs two distinct scene views from a single fused random measurement.The reconstruction task is decomposed into two sub-optimization problems and addressed using an iterative plug-and-play algorithm based on proximal gradient descent,incorporating image estimation and cross-view information interaction mechanisms.Dynamic message fusion is achieved through momentum feedback and residual adjustment.Experimental results demonstrate that,compared with other advanced single-view compressed sensing algorithms,the proposed method achieves higher reconstruction quality at low sampling rates,with a maximum PSNR improvement of 2.66 dB at a 10% compression ratio on the classic benchmark dataset Set11.

Key words: Compressed sensing, Dual-view CS framework, Proximal gradient descent, Message fusion

CLC Number: 

  • TP391
[1]ZHANG Z,ZHENG S,QIU M,et al.A decade review of video compressive sensing:Aroadmap to practical applications[J].Engineering,2025,46:172-185.
[2]ZHOU Y,XU C,DAI Y,et al.Dual-view stereovision-guided automatic inspection system for overhead transmission line corridor[J].Remote Sensing,2022,14(16):4095.
[3]KIVIHARJU P.Dual View Capsule Endoscope Optics with Metallic Mirrors that Can Serve as Loop Antennas for Wireless Power Transfer[D].Sähkötekniikan korkeakoulu/ELEC,2023.
[4]ZHANG S,HUANG H,FU Y.Fast parallel implementation of dual-camera compressive hyperspectral imaging system[J].IEEE Transactions on Circuits and Systems for Video Technology,2018,29(11):3404-3414.
[5]LI X D,DUNKIN F,DEZERT J.Multi-source information fusion:Progress and future[J].Chinese Journal of Aeronautics,2024,37(7):24-58.
[6]PARK J Y,WAKIN M B.A geometric approach to multi-view compressive imaging[J].EURASIP Journal on Advances in Signal Processing,2012(1):1-15.
[7]BARANIUK R G,CEVHER V,DUARTE M F,et al.Model-based compressive sensing[J].IEEE Transactions on Information Theory,2010,56(4):1982-2001.
[8]WANG Z B,TIAN Y L,WANG R F,et al.Image Compressed Sensing Attention Neural Network Based on Residual Feature Aggregation[J].Computer Science,2023,50(4):117-124.
[9]GAN H,GAO Y,LIU C,et al.Auto BCS:Block-based imagecompressive sensing with data-driven acquisition and noniterative reconstruction[J].IEEE Transactions on Cybernetics,2021,53(4):2558-2571.
[10]DU X L,ZHU J Y,GAO X,et al.A video compressed sensing method with integrated Deformable 3D Convolution and Transformer[J].Computer Science,2025,52(11):150-156.
[11]KULKARNI K,LOHIT S,TURAGA P,et al.Reconnet:Non-iterative reconstruction of images from compressively sensed measurements[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:449-458.
[12]KIM Y,SOH J W,PARK G Y,et al.Transfer learning from synthetic to real-noise denoising with adaptive instance normalization[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:3482-3492.
[13]GUO Z,GAN H.CPP-Net:Embracing Multi-Scale Feature Fusion into Deep Unfolding CP-PPA Network for Compressive Sensing[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2024:25086-25095.
[14]KONG S,WANG W,FENG X,et al.Deep red unfolding network for image restoration[J].IEEE Transactions on Image Processing,2021,31:852-867.
[15]ZHANG Z,LIU Y,LIU J,et al.AMP-Net:Denoising-baseddeep unfolding for compressive image sensing[J].IEEE Transactions on Image Processing,2020,30:1487-1500.
[16]YUAN T H,GAN Z L.Infraredand Visible Deep Unfolding Image Fusion Network Basedon Joint Enhancement Image Pair[J].Computer Science,2024,51(10):311-319.
[17]TIAN J P,HOU B J.Compressive sensing image reconstruction based on deep unfolding self-attention network[J].Journal of Jilin University(Engineering and Technology Edition),2024,54(10):3018-3026.
[18]HOSNY S,EL-KHARASHI M W,ABDEL-HAMID A T.Survey on compressed sensing over the past two decades[J].Memories-Materials,Devices,Circuits and Systems,2023,4:100060.
[19]ZHANG Z,DENG C,LIU Y,et al.Ten-mega-pixel snapshotcompressive imaging with a hybrid coded aperture[J].Photonics Research,2021,9(11):2277-2287.
[20]QIAO M,LIU X,YUAN X.Snapshot spatial-temporal compressive imaging[J].Optics letters,2020,45(7):1659-1662.
[21]CANDES E J.The restricted isometry property and its implications for compressed sensing[J].Comptes Rendus.Mathematique,2008,346(9/10):589-592.
[22]BOOMINATHAN V,ROBINSON J T,WALLER L,et al.Recent advances in lensless imaging[J].Optica,2021,9(1):1-16.
[23]CHEN Z,HOU X,SHAO L,et al.Compressive sensing multi-layer residual coefficients for image coding[J].IEEE Transactions on Circuits and Systems for Video Technology,2019,30(4):1109-1120.
[24]ZHANG K,ZUO W,GU S,et al.Learning deep CNN denoiser prior for image restoration[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:3929-3938.
[25]LI Q,ZHOU Y,LIANG Y,et al.Convergence analysis of proximal gradient with momentum for nonconvex optimization[C]//International Conference on Machine Learning.PMLR,2017:2111-2119.
[26]CHEN B,SONG J,XIE J F,et al.Deep physics-guided unrolling generalization for compressed sensing[J].International Journal of Computer Vision,2023,131(11):2864-2887.
[27]METZLER C,MOUSAVI A,BARANIUK R.Learned D-AMP:Principled neural network based compressive image recovery[C]//NIPS.2017.
[28]CHEN Z,GUO W L,FENG Y J,et al.Deep-learned regularization and proximal operator for image compressive sensing[J].IEEE Transactions on Image Processing,2021,30:7112-7126.
[29]BOOMINATHAN V,ROBINSON J T,WALLER L,et al.Recent advances in lensless imaging[J].Optica,2021,9(1):1-16.
[30]MALEKI A,MONTANARI A.Analysis of approximate message passing algorithm[C]//2010 44th Annual Conference on Information Sciences and Systems(CISS).IEEE,2010:1-7.
[31]DONOHO D L,MALEKI A,MONTANARI A.Message-passing algorithms for compressed sensing[J].Proceedings of the National Academy of Sciences,2009,106(45):18914-18919.
[32]ZHANG K,LI Y,ZUO W,et al.Plug-and-play image restoration with deep denoiser prior[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2021,44(10):6360-6376.
[33]GAVASKAR R G,CHAUDHURY K N.Plug-and-play ISTA converges with kernel denoisers[J].IEEE Signal Processing Letters,2020,27:610-614.
[34]RAMANI S,BU T,UNSER M.Monte-carlo sure:A black-box optimization of regularization parameters for general denoising algorithms[J].IEEE Transactions on Image Processing,2008,17(9):1540-1554.
[35]METZLER C A,MALEKI A,BARANIUK R G.From denoising to compressed sensing[J].IEEE Transactions on Information Theory,2016,62(9):5117-5144.
[36]KULKARNI K,LOHIT S,TURAGA P,et al.Reconnet:Non-iterative reconstruction of images from compressively sensed measurements[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:449-458.
[37]HUANG J B,SINGH A,AHUJA N.Single image super-resolution from transformed self-exemplars[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2015:5197-5206.
[38]RAMANI S,BLU T,UNSER M.Monte-Carlo SURE:A black-box optimization of regularization parameters for general denoising algorithms[J].IEEE Transactions on Image Processing,2008,17(9):1540-1554.
[39]SHEN M,GAN H,NING C,et al.TransCS:A transformer-based hybrid architecture for image compressed sensing[J].IEEE Transactions on Image Processing,2022,31:6991-7005.
[40]WANG X,ZHAO L,ZHANG J,et al.A wavelet-domain consistency-constrained compressive sensing framework based on memory-boosted guidance filtering[J].IEEE Transactions on Instrumentation and Measurement,2024,73:1-16.
[41]XU L,MEI H,TONG F,et al.DCCT-Net:A network combined dynamic CNN and transformer for image compressive sensing[C]//IEEE International Conference on Acoustics,Speech and Signal Processing.2025:1-5.
[42]ZHANG J,ZHAO C,GAO W.Optimization-inspired compactdeep compressive sensing[J].IEEE Journal of Selected Topics in Signal Processing,2020,14(4):765-774.
[1] FAN Haiju, YUE Shuang, DOU Yuqiang, LI Ming, ZHANG Mingzhu. Image Encryption Algorithm Based on Novel Chaotic System and Binary Block CompressedSensing [J]. Computer Science, 2025, 52(12): 400-410.
[2] LANG Aoqi, HUANG Weijie, YU Zhiyong, HUANG Fangwan. Spatiotemporal Active-sampling and Joint Inference of Urban Air Quality Data [J]. Computer Science, 2025, 52(11A): 241000116-9.
[3] HUANG Weijie, GUO Xianwei, YU Zhiyong, HUANG Fangwan. Active Sampling of Air Quality Based on Compressed Sensing Adaptive Measurement Matrix [J]. Computer Science, 2024, 51(7): 116-123.
[4] REN Bing, GUO Yan, LI Ning, LIU Cuntao. Method for Correlation Data Imputation Based on Compressed Sensing [J]. Computer Science, 2023, 50(7): 82-88.
[5] PAN Tao, TONG Xiaojun, ZHANG Miao, WANG Zhu. Image Compression and Encryption Based on Compressive Sensing and Hyperchaotic System [J]. Computer Science, 2023, 50(6A): 220200121-6.
[6] WANG Zhenbiao, QIN Yali, WANG Rongfang, ZHENG Huan. Image Compressed Sensing Attention Neural Network Based on Residual Feature Aggregation [J]. Computer Science, 2023, 50(4): 117-124.
[7] PAN Ze-min, QIN Ya-li, ZHENG Huan, WANG Rong-fang, REN Hong-liang. Block-based Compressed Sensing of Image Reconstruction Based on Deep Neural Network [J]. Computer Science, 2022, 49(11A): 210900118-9.
[8] LIU Yu-hong,LIU Shu-ying,FU Fu-xiang. Optimization of Compressed Sensing Reconstruction Algorithms Based on Convolutional Neural Network [J]. Computer Science, 2020, 47(3): 143-148.
[9] WU Xue-lin, ZHU Rong, GUO Ying. Ghost Imaging Reconstruction Algorithm Based on Block Sparse Bayesian Model [J]. Computer Science, 2020, 47(11A): 188-191.
[10] HOU Ming-xing,QI Hui,HUANG Bin-ke. Data Abnormality Processing in Wireless Sensor Networks Based on Distributed Compressed Sensing [J]. Computer Science, 2020, 47(1): 276-280.
[11] LI Xiu-qin, WANG Tian-jing, BAI Guang-wei, SHEN Hang. Two-phase Multi-target Localization Algorithm Based on Compressed Sensing [J]. Computer Science, 2019, 46(5): 50-56.
[12] WANG Peng-fei, ZHANG Hang. Sub-sampling Signal Reconstruction Based on Principal Component Under Underdetermined Conditions [J]. Computer Science, 2019, 46(10): 103-108.
[13] HENG Yang, CHEN Feng, XU Jian-feng, TANG Min. Application Status and Development Trends of Cardiac Magnetic Resonance Fast Imaging Based on Compressed Sensing Theory [J]. Computer Science, 2019, 46(1): 36-44.
[14] DU Xiu-li, HU Xing, CHEN Bo, QIU Shao-ming. Multi-hypothesis Reconstruction Algorithm of DCVS Based on Weighted Non-local Similarity [J]. Computer Science, 2019, 46(1): 291-296.
[15] DU Xiu-li, ZHANG Wei, GU Bin-bin, CHEN Bo, QIU Shao-ming. GLCM-based Adaptive Block Compressed Sensing Method for Image [J]. Computer Science, 2018, 45(8): 277-282.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!