Deep Network Cascade for Image Super-Resolution
Abstract
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.
Cite
Text
Cui et al. "Deep Network Cascade for Image Super-Resolution." European Conference on Computer Vision, 2014. doi:10.1007/978-3-319-10602-1_4Markdown
[Cui et al. "Deep Network Cascade for Image Super-Resolution." European Conference on Computer Vision, 2014.](https://mlanthology.org/eccv/2014/cui2014eccv-deep/) doi:10.1007/978-3-319-10602-1_4BibTeX
@inproceedings{cui2014eccv-deep,
title = {{Deep Network Cascade for Image Super-Resolution}},
author = {Cui, Zhen and Chang, Hong and Shan, Shiguang and Zhong, Bineng and Chen, Xilin},
booktitle = {European Conference on Computer Vision},
year = {2014},
pages = {49-64},
doi = {10.1007/978-3-319-10602-1_4},
url = {https://mlanthology.org/eccv/2014/cui2014eccv-deep/}
}