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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