  Interactive image restoration aims to generate restored images by adjusting a
controlling coefficient which determines the restoration level. Previous works
are restricted in modulating image with a single coefficient. However, real
images always contain multiple types of degradation, which cannot be well
determined by one coefficient. To make a step forward, this paper presents a
new problem setup, called multi-dimension (MD) modulation, which aims at
modulating output effects across multiple degradation types and levels.
Compared with the previous single-dimension (SD) modulation, the MD is setup to
handle multiple degradations adaptively and relief unbalanced learning problem
in different degradations. We also propose a deep architecture - CResMD with
newly introduced controllable residual connections for multi-dimension
modulation. Specifically, we add a controlling variable on the conventional
residual connection to allow a weighted summation of input and residual. The
values of these weights are generated by another condition network. We further
propose a new data sampling strategy based on beta distribution to balance
different degradation types and levels. With corrupted image and degradation
information as inputs, the network can output the corresponding restored image.
By tweaking the condition vector, users can control the output effects in MD
space at test time. Extensive experiments demonstrate that the proposed CResMD
achieve excellent performance on both SD and MD modulation tasks. Code is
available at https://github.com/hejingwenhejingwen/CResMD.
