Perception-Distortion Balanced ADMM Optimization for Single-Image Super-Resolution
Abstract
In image super-resolution, both pixel-wise accuracy and perceptual fidelity are desirable. However, most deep learning methods only achieve high performance in one aspect due to the perception-distortion trade-off, and works that successfully balance the trade-off rely on fusing results from separately trained models with ad-hoc post-processing. In this paper, we propose a novel super-resolution model with a low-frequency constraint (LFc-SR), which balances the objective and perceptual quality through a single model and yields super-resolved images with high PSNR and perceptual scores. We further introduce an ADMM-based alternating optimization method for the non-trivial learning of the constrained model. Experiments showed that our method, without cumbersome post-processing procedures, achieved the state-of-the-art performance. The code is available at https://github.com/Yuehan717/PDASR.
Cite
Text
Zhang et al. "Perception-Distortion Balanced ADMM Optimization for Single-Image Super-Resolution." Proceedings of the European Conference on Computer Vision (ECCV), 2022. doi:10.1007/978-3-031-19797-0_7Markdown
[Zhang et al. "Perception-Distortion Balanced ADMM Optimization for Single-Image Super-Resolution." Proceedings of the European Conference on Computer Vision (ECCV), 2022.](https://mlanthology.org/eccv/2022/zhang2022eccv-perceptiondistortion/) doi:10.1007/978-3-031-19797-0_7BibTeX
@inproceedings{zhang2022eccv-perceptiondistortion,
title = {{Perception-Distortion Balanced ADMM Optimization for Single-Image Super-Resolution}},
author = {Zhang, Yuehan and Ji, Bo and Hao, Jia and Yao, Angela},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2022},
doi = {10.1007/978-3-031-19797-0_7},
url = {https://mlanthology.org/eccv/2022/zhang2022eccv-perceptiondistortion/}
}