  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.
