基于卷积神经网络的压缩感知重构算法优化

doi:10.11896/jsjkx.190100199

摘要/Abstract

摘要： 压缩感知理论因其编码复杂度低、节省资源、抗干扰能力强等特点,被广泛应用于图像和视频信号处理。然而,传统的压缩感知技术也面临着重构时间长、算法复杂度高、迭代次数多、计算量大等问题。针对图像重构时间和重构质量的问题,文中提出一种新的卷积神经网络结构Combine Network (CombNet),它将压缩感知的测量值作为卷积神经网络的输入,连接一个全连接层,然后通过CombNet获得最终输出。实验结果表明,CombNet具有较低的复杂度及较好的恢复性能,在相同的采样率下,CombNet的峰值信噪比(PSNR)较TVAL3提高了7.2%~13.95%,较D-AMP提高了7.72%~174.84%。CombNet重构的耗时比传统重构算法提高了3个数量级,实现了实时重构。在采样率极低(采样率为0.01时)的情况下,CombNet的平均PSNR较D-AMP高出11.982dB,因此所提算法具有更好的视觉吸引力。

关键词: 峰值信噪比, 卷积神经网络, 实时重构, 压缩感知

Abstract: Compressed sensing theory is widely used in image and video signal processing because of its low coding complexity,resource saving and strong anti-jamming ability.However,the traditional compressed sensing technology also faces such problems as long reconstruction time,high algorithm complexity,multiple iterations and large amount of computation.Aiming at the time and quality of image reconstruction,a new convolutional neural network structure named Combine Network (CombNet) was proposed.It takes the measured values of compressed sensing as the input of convolutional neural network,connects a full connection layer,and then obtains the final output through CombNet.Experiment results show that CombNet has lower complexity and better recovery performance.At the same sampling rate,the peak signal-to-noise ratio (PSNR) of CombNet is 7.2%~13.95% higher than that of TVAL3,and 7.72%~174.84% higher than that of D-AMP.The reconstruction time of CombNe is three orders of magnitude higher than that of traditional reconstruction algorithm.When the sampling rate is very low (the sampling rate is 0.01),the average PSNR of CombNet is 11.982dB higher than D-AMP,therefore the proposed algorithm has better visual attraction.

Key words: Compressed sensing, Convolutional neural networks, PSNR, Real time reconstruction

中图分类号:

TP389.1

刘玉红,刘树英,付福祥. 基于卷积神经网络的压缩感知重构算法优化[J]. 计算机科学, 2020, 47(3): 143-148. https://doi.org/10.11896/jsjkx.190100199

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. https://doi.org/10.11896/jsjkx.190100199

0
/ / 推荐

导出引用管理器 EndNote|Reference Manager|ProCite|BibTeX|RefWorks

链接本文: https://www.jsjkx.com/CN/10.11896/jsjkx.190100199

https://www.jsjkx.com/CN/Y2020/V47/I3/143

参考文献

[1]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.
[2]DONOHO D L.Compressed sensing[J].IEEE Transactions on Information Theory,2006,52(4):1289-1306.
[3]WANG H L,WANG S,LIU W Y.An Overview of Compressed Sensing Implementation and Application[J].Journal of Detection & Control,2014,36(4).
[4]MOUSAVI A,BARANIUK R G.Learning to Invert:Signal Recovery Via Deep ConvoLutional Networks[C]∥2017 IEEE International Conference on Acoustics,Speech and Signal Proces-sing (ICASSP).2017.
[5]MALLAT S G,ZHANG Z.Matching pursuits with time-fre- quency dictionaries[J].IEEE Trans on Signal Processing,1993,41(12):3397-3415.
[6]TROPP J A,GILBERT A C.Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit[J].IEEE Transactions on Information Theory,2007,53(12):4655-4666.
[7]BLUMENSATH T,DAVIES M E.Iterative hard thresholding for compressed sensing[J].Applied & Computational Harmonic Analysis,2009,27(3):265-274.
[8]FIGUEIREDO M A T,NOWAK R D,WRIGHT S J.Gradient projection for sparse reconstruction:application to compressed sensing and other inverse problems[J].IEEE Journal of Selected Topics in Signal Processing,2008,1(4):586-597.
[9]MAIRAL J,BACH F,PONCE J,et al.Non-local sparse models for image restoration[C]∥IEEE International Conference on Computer Vision.2010.
[10]ELDAR Y C,KUPPINGER P,BÖLCSKEI H.Block-Sparse Signals:Uncertainty Relations and Efficient Recovery[J].IEEE Transactions on Signal Processing,2010,58(6):3042-3054.
[11]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.
[12]LONG J,SHELHAMER E,DARRELL T.Fully convolutional networks for semantic segmentation[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2014,39(4):640-651.
[13]EIGEN D,PUHRSCH C,FERGUS R.Depth Map Prediction from a Single Image using a Multi-Scale Deep Networ[J].ar-Xiv:1406.2283v1.
[14]CHAO D,HE K,CHEN C L,et al.Learning a Deep Convolutional Network for Image Super-Resolution[C]∥European Conference on Computer Vision.Cham:Springer,2014.
[15]XIE J Y,XU L L,CHEN E H.Image denoising and inpainting with deep neural networks[C]∥International Conference on Neural Information Processing Systems.2012:341-349.
[16]MOUSAVI A,PATEL A B,BARANIUK R G.A deep learning approach to structured signal recovery[C]∥2015 53rd Annual Allerton Conference on Communication,Control,and Computing (Allerton).IEEE,2015:1336-1343.
[17]KULKARNI K,LOHIT S,TURAGA P,et al.ReconNet:Non-Iterative Reconstruction of Images from Compressively Sensed Measurements[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).IEEE Computer Society,Las Vegas,NV,2016.
[18]LI Y D,HAO Z B,LEI H.Survey of convolutional neural network[J].Journal of Computer Application,2016,36(9):2508-2515.
[19]TAQI A M,AL-AZZO F,MARIOFANNA M,et al.Classification and discrimination of focal and non-focal EEG signals based on deep neural network[C]∥International Conference on Current Research in Computer Science & Information Technology.2017:8692.
[20]JIA Y Q,SHELHAMER E,DONAHUE J,et al.Caffe:Convolutional Architecture for Fast Feature Embedding[C]∥ACM International Conference on Multimedia.2014.
[21]LI C B,YIN W T,JIANG H,et al.An efficient augmented Lagrangian method with applications to total variation minimization[J].Computational Optimization and Applications,2013,56(3):507-530.
[22]METZLER C A,MALEKI A,BARANIUK R G.From Denoi- sing to Compressed Sensing[J].IEEE Transactions on Information Theory,2016,62(9):5117-5144.
[23]DABOV K,FOI A,KATKOVNIK V,et al.Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering[J].IEEE Transactions on Image Processing,2007,16(8):2080-2095.

相关文章 15

[1]	周乐员, 张剑华, 袁甜甜, 陈胜勇. 多层注意力机制融合的序列到序列中国连续手语识别和翻译 Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion 计算机科学, 2022, 49(9): 155-161. https://doi.org/10.11896/jsjkx.210800026
[2]	李宗民, 张玉鹏, 刘玉杰, 李华. 基于可变形图卷积的点云表征学习 Deformable Graph Convolutional Networks Based Point Cloud Representation Learning 计算机科学, 2022, 49(8): 273-278. https://doi.org/10.11896/jsjkx.210900023
[3]	陈泳全, 姜瑛. 基于卷积神经网络的APP用户行为分析方法 Analysis Method of APP User Behavior Based on Convolutional Neural Network 计算机科学, 2022, 49(8): 78-85. https://doi.org/10.11896/jsjkx.210700121
[4]	朱承璋, 黄嘉儿, 肖亚龙, 王晗, 邹北骥. 基于注意力机制的医学影像深度哈希检索算法 Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism 计算机科学, 2022, 49(8): 113-119. https://doi.org/10.11896/jsjkx.210700153
[5]	檀莹莹, 王俊丽, 张超波. 基于图卷积神经网络的文本分类方法研究综述 Review of Text Classification Methods Based on Graph Convolutional Network 计算机科学, 2022, 49(8): 205-216. https://doi.org/10.11896/jsjkx.210800064
[6]	张颖涛, 张杰, 张睿, 张文强. 全局信息引导的真实图像风格迁移 Photorealistic Style Transfer Guided by Global Information 计算机科学, 2022, 49(7): 100-105. https://doi.org/10.11896/jsjkx.210600036
[7]	戴朝霞, 李锦欣, 张向东, 徐旭, 梅林, 张亮. 基于DNGAN的磁共振图像超分辨率重建算法 Super-resolution Reconstruction of MRI Based on DNGAN 计算机科学, 2022, 49(7): 113-119. https://doi.org/10.11896/jsjkx.210600105
[8]	刘月红, 牛少华, 神显豪. 基于卷积神经网络的虚拟现实视频帧内预测编码 Virtual Reality Video Intraframe Prediction Coding Based on Convolutional Neural Network 计算机科学, 2022, 49(7): 127-131. https://doi.org/10.11896/jsjkx.211100179
[9]	徐鸣珂, 张帆. Head Fusion:一种提高语音情绪识别的准确性和鲁棒性的方法 Head Fusion:A Method to Improve Accuracy and Robustness of Speech Emotion Recognition 计算机科学, 2022, 49(7): 132-141. https://doi.org/10.11896/jsjkx.210100085
[10]	金方焱, 王秀利. 融合RACNN和BiLSTM的金融领域事件隐式因果关系抽取 Implicit Causality Extraction of Financial Events Integrating RACNN and BiLSTM 计算机科学, 2022, 49(7): 179-186. https://doi.org/10.11896/jsjkx.210500190
[11]	杨玥, 冯涛, 梁虹, 杨扬. 融合交叉注意力机制的图像任意风格迁移 Image Arbitrary Style Transfer via Criss-cross Attention 计算机科学, 2022, 49(6A): 345-352. https://doi.org/10.11896/jsjkx.210700236
[12]	杨健楠, 张帆. 一种结合双注意力机制和层次网络结构的细碎农作物分类方法 Classification Method for Small Crops Combining Dual Attention Mechanisms and Hierarchical Network Structure 计算机科学, 2022, 49(6A): 353-357. https://doi.org/10.11896/jsjkx.210200169
[13]	王杉, 徐楚怡, 师春香, 张瑛. 基于CNN-LSTM的卫星云图云分类方法研究 Study on Cloud Classification Method of Satellite Cloud Images Based on CNN-LSTM 计算机科学, 2022, 49(6A): 675-679. https://doi.org/10.11896/jsjkx.210300177
[14]	孙福权, 崔志清, 邹彭, 张琨. 基于多尺度特征的脑肿瘤分割算法 Brain Tumor Segmentation Algorithm Based on Multi-scale Features 计算机科学, 2022, 49(6A): 12-16. https://doi.org/10.11896/jsjkx.210700217
[15]	吴子斌, 闫巧. 基于动量的映射式梯度下降算法 Projected Gradient Descent Algorithm with Momentum 计算机科学, 2022, 49(6A): 178-183. https://doi.org/10.11896/jsjkx.210500039

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed