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

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