Research on Image Reconstruction of Compressed Sensing Based on a Multi-Feature Residual Network
College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China
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Sensors 2020, 20(15), 4202; https://doi.org/10.3390/s20154202
Received: 11 June 2020
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Revised: 19 July 2020
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Accepted: 23 July 2020
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Published: 28 July 2020
(This article belongs to the Section Sensing and Imaging)
In order to solve the problem of how to quickly and accurately obtain crop images during crop growth monitoring, this paper proposes a deep compressed sensing image reconstruction method based on a multi-feature residual network. In this method, the initial reconstructed image obtained by linear mapping is input to a multi-feature residual reconstruction network, and multi-scale convolution is used to autonomously learn different features of the crop image to realize deep reconstruction of the image, and complete the inverse solution of compressed sensing. Compared with traditional image reconstruction methods, the deep learning-based method relaxes the assumptions about the sparsity of the original crop image and converts multiple iterations into deep neural network calculations to obtain higher accuracy. The experimental results show that the compressed sensing image reconstruction method based on the multi-feature residual network proposed in this paper can improve the quality of crop image reconstruction.
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MDPI and ACS Style
Nan, R.; Sun, G.; Wang, Z.; Ren, X. Research on Image Reconstruction of Compressed Sensing Based on a Multi-Feature Residual Network. Sensors 2020, 20, 4202. https://doi.org/10.3390/s20154202
AMA Style
Nan R, Sun G, Wang Z, Ren X. Research on Image Reconstruction of Compressed Sensing Based on a Multi-Feature Residual Network. Sensors. 2020; 20(15):4202. https://doi.org/10.3390/s20154202
Chicago/Turabian StyleNan, Ruili, Guiling Sun, Zhihong Wang, and Xiangnan Ren. 2020. "Research on Image Reconstruction of Compressed Sensing Based on a Multi-Feature Residual Network" Sensors 20, no. 15: 4202. https://doi.org/10.3390/s20154202
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