  Single-image super-resolution is the process of increasing the resolution of
an image, obtaining a high-resolution (HR) image from a low-resolution (LR)
one. By leveraging large training datasets, convolutional neural networks
(CNNs) currently achieve the state-of-the-art performance in this task. Yet,
during testing/deployment, they fail to enforce consistency between the HR and
LR images: if we downsample the output HR image, it never matches its LR input.
Based on this observation, we propose to post-process the CNN outputs with an
optimization problem that we call TV-TV minimization, which enforces
consistency. As our extensive experiments show, such post-processing not only
improves the quality of the images, in terms of PSNR and SSIM, but also makes
the super-resolution task robust to operator mismatch, i.e., when the true
downsampling operator is different from the one used to create the training
dataset.
