Perception-Distortion Balanced ADMM Optimization for Single-Image
Super-Resolution
- URL: http://arxiv.org/abs/2208.03324v1
- Date: Fri, 5 Aug 2022 05:37:55 GMT
- Title: Perception-Distortion Balanced ADMM Optimization for Single-Image
Super-Resolution
- Authors: Yuehan Zhang, Bo Ji, Angela Yao
- Abstract summary: We propose a novel super-resolution model with a low-frequency constraint (LFc-SR)
We 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.
- Score: 29.19388490351459
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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