  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
https://github.com/dvlab-research/Simple-SR.
