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多尺度生成对抗网络下图像压缩感知重建算法 被引量:1

Multi-scale generative adversarial network for image compressed sensing and reconstruction algorithm
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摘要 针对目前基于深度学习的压缩感知重建网络存在单通道重建网络没有深入挖掘图像的多尺度特征,缺乏对重建网络的反馈机制,并且重建网络缺乏与测量矩阵的关联,制约了重建质量的进一步提升的问题,提出了一种多尺度生成对抗网络下图像压缩感知与重建算法。该算法先通过多通道残差块提取图像的多尺度信息,加入判别网络形成对多尺度生成网络的反馈,再将全卷积测量网络与重建网络联合训练,以提升图像重建质量。实验结果表明:本文方法相对于ISTA-Net+方法在3种采样率下重建精度提高了2.02~4.09 dB。 The current deep learning-based compressed sensing and reconstruction network mainly has the following problems:the single-channel reconstruction network does not deeply explore the multi-scale features of the image,lacks a feedback mechanism for the reconstruction network,and the reconstruction network lacks correlation with the measurement matrix,which restricts the further improvement of the reconstruction quality.Therefore,an image compressed sensing and reconstruction algorithm based on multi-scale generation adversarial network is proposed,which extracts the multi-scale information of images through multi-channel residual blocks,joins the discriminant network to form a feedback to the multi-scale generative network,and then trains the full convolutional measurement network jointly with the reconstruction network to improve the image reconstruction quality.Experimental results show that the reconstruction accuracy of the proposed method is improved by 2.02-4.09 dB compared with ISTA-Net+method under three sampling rates.
作者 曾春艳 严康 王志锋 王正辉 ZENG Chun-yan;YAN Kang;WANG Zhi-feng;WANG Zheng-hui(Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System,Hubei University of Technology,Wuhan 430068,China;School of Education Information Technology,Central China Normal University,Wuhan 430079,China)
出处 《吉林大学学报(工学版)》 EI CAS CSCD 北大核心 2023年第10期2923-2931,共9页 Journal of Jilin University:Engineering and Technology Edition
基金 国家自然科学基金项目(61901165,62177022,61501199) 湖北省自然科学基金项目(2022CFA007) 武汉市知识创新项目(2022020801010258) 华中师范大学人工智能助推教师队伍建设行动试点项目(CCNUAI&FE2022-03-01)。
关键词 计算机应用 压缩感知 残差网络 生成对抗网络 computer application compressed sensing residual network generative adversarial network
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