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Infrared long-wave multispectral image reconstruction based on auto-encoder network
Author(s): Yuan Tian; Hanlin Qin; Lin Ma; Shuowen Yang; Yang Yue; Wenxing Nie; Jiawei Zhang
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Paper Abstract

Due to the problems of long iteration time and poor image quality in the traditional infrared multispectral image reconstruction method based on compressed sensing(CS), an auto-encoders network based on residuals is proposed. Autoencoders are unsupervised neural networks where the output and input layers share the same number of nodes, and which can reconstruct its own inputs through encoder and decoder functions. using code decoding technique learn from real infrared multispectral image spectrum information, through the fast image reconstruction of auto-encoder, get high quality image. The performance of the method is verified by using multiple infrared multispectral images. The results show that the method has the advantages of high image processing efficiency and high spatial resolution. Compared with the traditional compressed sensing method, the auto-encoder network based on residuals has better effect on infrared multispectral image reconstruction.

Paper Details

Date Published: 18 December 2019
PDF: 7 pages
Proc. SPIE 11342, AOPC 2019: AI in Optics and Photonics, 113420R (18 December 2019); doi: 10.1117/12.2548160
Show Author Affiliations
Yuan Tian, Xidian Univ. (China)
Hanlin Qin, Xidian Univ. (China)
Lin Ma, Xidian Univ. (China)
Shuowen Yang, Xidian Univ. (China)
Yang Yue, Xidian Univ. (China)
Wenxing Nie, Xidian Univ. (China)
Jiawei Zhang, Xidian Univ. (China)


Published in SPIE Proceedings Vol. 11342:
AOPC 2019: AI in Optics and Photonics
John Greivenkamp; Jun Tanida; Yadong Jiang; HaiMei Gong; Jin Lu; Dong Liu, Editor(s)

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