论文标题
对单图像超分辨率的感知延伸平衡ADMM优化
Perception-Distortion Balanced ADMM Optimization for Single-Image Super-Resolution
论文作者
论文摘要
在图像超分辨率中，需要像素的精度和感知忠诚度。但是，大多数深度学习方法仅在一个方面才能在一个方面实现高性能，并且由于感知能力的权衡，成功平衡权衡取舍的工作取决于从单独培训的模型和临时后处理的融合。在本文中，我们提出了一个具有低频约束（LFC-SR）的新型超分辨率模型，该模型通过单个模型平衡了客观和感知质量，并产生具有较高PSNR和知觉得分的超级分辨图像。我们进一步介绍了一种基于ADMM的交替优化方法，用于对受约束模型的非平凡学习。实验表明，我们的方法没有繁琐的后处理程序，可以实现最新的性能。该代码可在https://github.com/yuehan717/pdasr上找到。
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.
