  We propose a simple, interpretable framework for solving a wide range of
image reconstruction problems such as denoising and deconvolution. Given a
corrupted input image, the model synthesizes a spatially varying linear filter
which, when applied to the input image, reconstructs the desired output. The
model parameters are learned using supervised or self-supervised training. We
test this model on three tasks: non-uniform motion blur removal,
lossy-compression artifact reduction and single image super resolution. We
demonstrate that our model substantially outperforms state-of-the-art methods
on all these tasks and is significantly faster than optimization-based
approaches to deconvolution. Unlike models that directly predict output pixel
values, the predicted filter flow is controllable and interpretable, which we
demonstrate by visualizing the space of predicted filters for different tasks.

Image and Video Processing;Computer Vision and Pattern Recognition;Graphics;Machine Learning