  We propose a simple extension of residual networks that works simultaneously
in multiple resolutions. Our network design is inspired by the iterative
back-projection algorithm but seeks the more difficult task of learning how to
enhance images. Compared to similar approaches, we propose a novel solution to
make back-projections run in multiple resolutions by using a data pipeline
workflow. Features are updated at multiple scales in each layer of the network.
The update dynamic through these layers includes interactions between different
resolutions in a way that is causal in scale, and it is represented by a system
of ODEs, as opposed to a single ODE in the case of ResNets. The system can be
used as a generic multi-resolution approach to enhance images. We test it on
several challenging tasks with special focus on super-resolution and raindrop
removal. Our results are competitive with state-of-the-arts and show a strong
ability of our system to learn both global and local image features.
