Based on the auto-encoder, shallow and deep auto-encoders with residual concept are constructed. The effects of different depth auto-encoders on large-field of view infrared image reconstruction are discussed under four conditions: no sparse constraint, regularization constraint, KL divergence sparse constraint and both constraints. From the reconstructed image quality and index parameters, it can be concluded that with the increase of the auto-encoder depth, sparse constraints have a greater impact on the reconstruction effect of the network.


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    Title :

    Super-resolution of large field of view infrared image based on convolutional sparse auto-encoder


    Contributors:
    Chen, Yu-dan (author) / Hu, Wen-gang (author) / Liu, Jie (author) / Yin, Jian-ling (author)

    Conference:

    Ninth Symposium on Novel Photoelectronic Detection Technology and Applications ; 2022 ; Hefei,China


    Published in:

    Proc. SPIE ; 12617


    Publication date :

    2023-04-04





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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