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18 December 2019 Infrared long-wave multispectral image reconstruction based on auto-encoder network
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Proceedings Volume 11342, AOPC 2019: AI in Optics and Photonics; 113420R (2019)
Event: Applied Optics and Photonics China (AOPC2019), 2019, Beijing, China
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.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yuan Tian, Hanlin Qin, Lin Ma, Shuowen Yang, Yang Yue, Wenxing Nie, and Jiawei Zhang "Infrared long-wave multispectral image reconstruction based on auto-encoder network", Proc. SPIE 11342, AOPC 2019: AI in Optics and Photonics, 113420R (18 December 2019);

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