Single image super-resolution (SISR) deals with a fundamental problem of
upsampling a low-resolution (LR) image to its high-resolution (HR) version.
Last few years have witnessed impressive progress propelled by deep learning
methods. However, one critical challenge faced by existing methods is to strike
a sweet spot of deep model complexity and resulting SISR quality. This paper
addresses this pain point by proposing a linearly-assembled pixel-adaptive
regression network (LAPAR), which casts the direct LR to HR mapping learning
into a linear coefficient regression task over a dictionary of multiple
predefined filter bases. Such a parametric representation renders our model
highly lightweight and easy to optimize while achieving state-of-the-art
results on SISR benchmarks. Moreover, based on the same idea, LAPAR is extended
to tackle other restoration tasks, e.g., image denoising and JPEG image
deblocking, and again, yields strong performance. The code is available at
this https URL

本文提出一个线性组装的像素自适应回归网络用于单幅图像超分辨率，将直接 LR to HR 映射学习转化为对多个预定义滤波器基字典的线性系数回归任务，同时将此思想扩展到处理其他图像修复任务中，例如图像去噪和 JPEG 图像去块运动估计并获得先进性能。