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arXiv · 2208.03324

Perception-Distortion Balanced ADMM Optimization for Single-Image Super-Resolution

Yuehan Zhang·Bo Ji·Jia Hao·Angela Yao
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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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Yuehan Zhang, Bo Ji, Jia Hao, Angela Yao. 2022-08-05. Perception-Distortion Balanced ADMM Optimization for Single-Image Super-Resolution. https://arxiv.org/abs/2208.03324

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