  Super-resolution (SR) is an ill-posed problem, which means that infinitely
many high-resolution (HR) images can be degraded to the same low-resolution
(LR) image. To study the one-to-many stochastic SR mapping, we implicitly
represent the non-local self-similarity of natural images and develop a
Variational Sparse framework for Super-Resolution (VSpSR) via neural networks.
Since every small patch of a HR image can be well approximated by the sparse
representation of atoms in an over-complete dictionary, we design a two-branch
module, i.e., VSpM, to explore the SR space. Concretely, one branch of VSpM
extracts patch-level basis from the LR input, and the other branch infers
pixel-wise variational distributions with respect to the sparse coefficients.
By repeatedly sampling coefficients, we could obtain infinite sparse
representations, and thus generate diverse HR images. According to the
preliminary results of NTIRE 2021 challenge on learning SR space, our team
(FudanZmic21) ranks 7-th in terms of released scores. The implementation of
VSpSR is released at https://zmiclab.github.io/.
