Multi-Scale Residual Reconstruction Neural Network With Non-Local Constraint
Multi-Scale Residual Reconstruction Neural Network With Non-Local Constraint
复制标题
非局部约束的多尺度残差重建神经网络
DOI：
10.1109/access.2019.2918593
复制
发表时间：
2019
期刊：
影响因子：
3.9
通讯作者：
Hu Fei
中科院分区：
文献类型：
--
作者：
Li Wan;Liu Fang;Jiao Licheng;Hu Fei
随着神经网络技术的发展，人们提出了一些新的重构网络来解决压缩感知（CS）重构问题。与传统的重建算法相比，该算法能够在较低的采样率下快速、准确地从压缩后的测量数据中重建出原始图像。然而，基于神经网络的CS重建算法忽略了图像的非局部相似性，这是重建的重要先验信息。提出了一种具有非局部约束的多尺度残差重构神经网络（NL_MRN）。该算法首先考虑先验图像的非局部相似性，在重构网络中加入非局部操作。然后，将具有不同卷积核大小的不同尺度残差重建模块组合以获得最终输出。最后将整个网络的损失函数定义为不同尺度重构模块的损失函数的加权和。此外，提出的分段训练方法提高了网络的训练效率。理论分析和实验结果表明，与其他重建算法相比，该算法能获得更好的重建效果，尤其是在低采样率下。
With the development of the neural network, some novel reconstructed networks are proposed to solve the problem of compressive sensing (CS) reconstruction. Compare with the traditional reconstruction algorithms, they can reconstruct the original images from the compressed measurement quickly and accurately with a low sampling rate. However, the CS reconstruction algorithms based on neural network ignore the image non-local similarity that is important prior information for the reconstruction. We propose a multi-scale residual reconstruction neural network with non-local constraint (NL_MRN). First, it considers the prior image non-local similarity and adds a non-local operation into the reconstruction network. Then, different scale residual reconstruction modules that have different convolution kernel size are combined to obtain the final output. Finally, the loss function of the whole network is defined as a weighted sum of the loss function of different scale reconstruction modules. What is more, the training efficiency of the network is improved by the proposed segmental training method. The theoretical analysis and the experimental results show that the proposed NL_MRN achieve better reconstruction compared with other reconstruction algorithms, especially at a low sampling rate.
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DOI：
10.1017/cbo9780511794308
发表时间：
2012
期刊：
--
影响因子：
--
作者：
Gitta Kutyniok
通讯作者：
Gitta Kutyniok
影响因子：
2.5
作者：
Needell, D.;Tropp, J. A.
通讯作者：
Tropp, J. A.
影响因子：
10.6
作者：
Jianbo Yang;X. Liao;Xin Yuan;P. Llull;D. Brady;G. Sapiro;L. Carin
通讯作者：
Jianbo Yang;X. Liao;Xin Yuan;P. Llull;D. Brady;G. Sapiro;L. Carin
影响因子：
10.6
作者：
Jin, Kyong Hwan;McCann, Michael T.;Unser, Michael
通讯作者：
Unser, Michael
影响因子：
5.4
作者：
He, Lihan;Carin, Lawrence
通讯作者：
Carin, Lawrence