  In this paper, we present D2C-SR, a novel framework for the task of
real-world image super-resolution. As an ill-posed problem, the key challenge
in super-resolution related tasks is there can be multiple predictions for a
given low-resolution input. Most classical deep learning based approaches
ignored the fundamental fact and lack explicit modeling of the underlying
high-frequency distribution which leads to blurred results. Recently, some
methods of GAN-based or learning super-resolution space can generate simulated
textures but do not promise the accuracy of the textures which have low
quantitative performance. Rethinking both, we learn the distribution of
underlying high-frequency details in a discrete form and propose a two-stage
pipeline: divergence stage to convergence stage. At divergence stage, we
propose a tree-based structure deep network as our divergence backbone.
Divergence loss is proposed to encourage the generated results from the
tree-based network to diverge into possible high-frequency representations,
which is our way of discretely modeling the underlying high-frequency
distribution. At convergence stage, we assign spatial weights to fuse these
divergent predictions to obtain the final output with more accurate details.
Our approach provides a convenient end-to-end manner to inference. We conduct
evaluations on several real-world benchmarks, including a new proposed
D2CRealSR dataset with x8 scaling factor. Our experiments demonstrate that
D2C-SR achieves better accuracy and visual improvements against
state-of-the-art methods, with a significantly less parameters number and our
D2C structure can also be applied as a generalized structure to some other
methods to obtain improvement. Our codes and dataset are available at
https://github.com/megvii-research/D2C-SR
