  In this paper, we present D2C-SR, a novel framework for the task of image
super-resolution(SR). As an ill-posed problem, the key challenge for
super-resolution related tasks is there can be multiple predictions for a given
low-resolution input. Most classical methods and early deep learning based
approaches ignored this fundamental fact and modeled this problem as a
deterministic processing which often lead to unsatisfactory results. Inspired
by recent works like SRFlow, we tackle this problem in a semi-probabilistic
manner and propose a two-stage pipeline: a divergence stage is used to learn
the distribution of underlying high-resolution outputs in a discrete form, and
a convergence stage is followed to fuse the learned predictions into a final
output. More specifically, we propose a tree-based structure deep network,
where each branch is designed to learn a possible high-resolution prediction.
At the divergence stage, each branch is trained separately to fit ground truth,
and a triple loss is used to enforce the outputs from different branches
divergent. Subsequently, we add a fuse module to combine the multiple
predictions as the outputs from the first stage can be sub-optimal. The fuse
module can be trained to converge w.r.t the final high-resolution image in an
end-to-end manner. We conduct evaluations on several benchmarks, including a
new proposed dataset with 8x upscaling factor. Our experiments demonstrate that
D2C-SR can achieve state-of-the-art performance on PSNR and SSIM, with a
significantly less computational cost.
