  Recent advances in deep learning have led to significant improvements in
single image super-resolution (SR) research. However, due to the amplification
of noise during the upsampling steps, state-of-the-art methods often fail at
reconstructing high-resolution images from noisy versions of their
low-resolution counterparts. However, this is especially important for images
from unknown cameras with unseen types of image degradation. In this work, we
propose to jointly perform denoising and super-resolution. To this end, we
investigate two architectural designs: "in-network" combines both tasks at
feature level, while "pre-network" first performs denoising and then
super-resolution. Our experiments show that both variants have specific
advantages: The in-network design obtains the strongest results when the type
of image corruption is aligned in the training and testing dataset, for any
choice of denoiser. The pre-network design exhibits superior performance on
unseen types of image corruption, which is a pathological failure case of
existing super-resolution models. We hope that these findings help to enable
super-resolution also in less constrained scenarios where source camera or
imaging conditions are not well controlled. Source code and pretrained models
are available at https://github.com/
angelvillar96/super-resolution-noisy-images.
