CMCS-net: image compressed sensing with convolutional measurement via DCNN

作     者：Xie, Yahong Wang, Hailin Wang, Jianjun 

作者机构：Southwest Univ Sch Math & Stat Chongqing Peoples R China Southwest Univ Inst Intelligent Finance & Digital Econ Chongqing Peoples R China 

出 版 物：《IET IMAGE PROCESSING》 (IET影像处理)

年 卷 期：2020年第14卷第15期

页      面：3839-3850页

核心收录：

学科分类：0808[工学-电气工程] 1002[医学-临床医学] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China [12071380, 61673015, 61273020, 11901476] Fundamental Research Funds for the Central Universities [XDJK2018C076, XDJK2020B033, SWU1809002] China Postdoctoral Science Foundation [2018M643390] 

主　　题：image representation neural nets computational complexity image coding compressed sensing image reconstruction convolution data compression learning (artificial intelligence) CMCS-net image compressed sensing DCNN deep learning methods compressed sensing image recovery stage compressed measurement stage huge measurement dictionary high computational complexity deep convolutional neural network convolution operation measurement phase reconstruction phase measurement matrix convolutional compressed measurement recovery phase nature images multilayered convolutional sparse coding 

摘      要：Recently, deep learning methods have made a remarkable improvement in compressed sensing image recovery stage. In the compressed measurement stage, the existing methods measured by block by block owing to a huge measurement dictionary for the whole images and the high computational complexity. In this work, a novel deep convolutional neural network (DCNN) named Convolutional Measurement Compressed Sensing network (CMCS-net) is proposed for image compressed sensing considering both convolutional measurement (CM) and sparse prior. Different from existing works, the convolution operation is adopted both in the measurement phase and reconstruction phase, which retains the structure information of images much better. Simultaneously, the size of the measurement matrix is no longer limited by data dimensions. Particularly, by unfolding the CM process to analyse a Toeplitz-type matrix, the theoretical support of the convolutional compressed measurement is proposed. In addition, in the recovery phase, the authors consider the sparse prior in nature images by embedding the truncated hierarchical projection algorithm into their architecture to solve the problem of multilayered convolutional sparse coding. Furthermore, extensive experiments demonstrate that their proposed CMCS-net can marvellously reconstruct the images and fully remove the block artefact.