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Deep Back-Projection Networks For Super-Resolution

We propose Deep Back-Projection Networks (DBPN), that exploit iterative up- and down-sampling layers, providing an error feedback mechanism for projection errors at each stage. We construct mutually-connected up- and down-sampling stages each of which represents different types of image degradation and high-resolution components.

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Edits on 6 Apr, 2022
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micky colby
edited on 6 Apr, 2022
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Deep Back-Projection Networks For Super-Resolution

We propose Deep Back-Projection Networks (DBPN), that exploit iterative up- and down-sampling layers, providing an error feedback mechanism for projection errors at each stage. We construct mutually-connected up- and down-sampling stages each of which represents different types of image degradation and high-resolution components.

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Repository
https://github.com/sanghyun-son/EDSR-PyTorchhttps://github.com/thstkdgus35/EDSR-PyTorch
Website
http://openaccess.thecvf.com/content_cvpr_2018/html/Haris_Deep_Back-Projection_Networks_CVPR_2018_paper.htmlhttp://openaccess.thecvf.com/content_cvpr_2018/papers/Haris_Deep_Back-Projection_Networks_CVPR_2018_paper.pdfhttps://arxiv.org/abs/1803.02735v1https://arxiv.org/pdf/1803.02735v1.pdf
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March 7, 2018

Deep Back-Projection Networks For Super-Resolution
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micky colby profile picture
micky colby
created this topic on 6 Apr, 2022
Edits made to:
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 Deep Back-Projection Networks For Super-Resolution

We propose Deep Back-Projection Networks (DBPN), that exploit iterative up- and down-sampling layers, providing an error feedback mechanism for projection errors at each stage. We construct mutually-connected up- and down-sampling stages each of which represents different types of image degradation and high-resolution components.

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