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2014 | OriginalPaper | Chapter

Deep Network Cascade for Image Super-resolution

Authors : Zhen Cui, Hong Chang, Shiguang Shan, Bineng Zhong, Xilin Chen

Published in: Computer Vision – ECCV 2014

Publisher: Springer International Publishing

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In this paper, we propose a new model called deep network cascade (DNC) to gradually upscale low-resolution images layer by layer, each layer with a small scale factor. DNC is a cascade of multiple stacked collaborative local auto-encoders. In each layer of the cascade, non-local self-similarity search is first performed to enhance high-frequency texture details of the partitioned patches in the input image. The enhanced image patches are then input into a collaborative local auto-encoder (CLA) to suppress the noises as well as collaborate the compatibility of the overlapping patches. By closing the loop on non-local self-similarity search and CLA in a cascade layer, we can refine the super-resolution result, which is further fed into next layer until the required image scale. Experiments on image super-resolution demonstrate that the proposed DNC can gradually upscale a low-resolution image with the increase of network layers and achieve more promising results in visual quality as well as quantitative performance.

Metadata
Title
Deep Network Cascade for Image Super-resolution
Authors
Zhen Cui
Hong Chang
Shiguang Shan
Bineng Zhong
Xilin Chen
Copyright Year
2014
Publisher
Springer International Publishing
DOI
https://doi.org/10.1007/978-3-319-10602-1_4

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