  Deep learning based methods have recently pushed the state-of-the-art on the
problem of Single Image Super-Resolution (SISR). In this work, we revisit the
more traditional interpolation-based methods, that were popular before, now
with the help of deep learning. In particular, we propose to use a
Convolutional Neural Network (CNN) to estimate spatially variant interpolation
kernels and apply the estimated kernels adaptively to each position in the
image. The whole model is trained in an end-to-end manner. We explore two ways
to improve the results for the case of large upscaling factors, and propose a
recursive extension of our basic model. This achieves results that are on par
with state-of-the-art methods. We visualize the estimated adaptive
interpolation kernels to gain more insight on the effectiveness of the proposed
method. We also extend the method to the task of joint image filtering and
again achieve state-of-the-art performance.
