  Multi-Grid Back-Projection (MGBP) is a fully-convolutional network
architecture that can learn to restore images and videos with upscaling
artifacts. Using the same strategy of multi-grid partial differential equation
(PDE) solvers this multiscale architecture scales computational complexity
efficiently with increasing output resolutions. The basic processing block is
inspired in the iterative back-projection (IBP) algorithm and constitutes a
type of cross-scale residual block with feedback from low resolution
references. The architecture performs in par with state-of-the-arts
alternatives for regression targets that aim to recover an exact copy of a high
resolution image or video from which only a downscale image is known. A
perceptual quality target aims to create more realistic outputs by introducing
artificial changes that can be different from a high resolution original
content as long as they are consistent with the low resolution input. For this
target we propose a strategy using noise inputs in different resolution scales
to control the amount of artificial details generated in the output. The noise
input controls the amount of innovation that the network uses to create
artificial realistic details. The effectiveness of this strategy is shown in
benchmarks and it is explained as a particular strategy to traverse the
perception-distortion plane.
