  Nowdays, most datasets used to train and evaluate super-resolution models are
single-modal simulation datasets. However, due to the variety of image
degradation types in the real world, models trained on single-modal simulation
datasets do not always have good robustness and generalization ability in
different degradation scenarios. Previous work tended to focus only on
true-color images. In contrast, we first proposed real-world black-and-white
old photo datasets for super-resolution (OID-RW), which is constructed using
two methods of manually filling pixels and shooting with different cameras. The
dataset contains 82 groups of images, including 22 groups of character type and
60 groups of landscape and architecture. At the same time, we also propose a
multi-modal degradation dataset (MDD400) to solve the super-resolution
reconstruction in real-life image degradation scenarios. We managed to simulate
the process of generating degraded images by the following four methods:
interpolation algorithm, CNN network, GAN network and capturing videos with
different bit rates. Our experiments demonstrate that not only the models
trained on our dataset have better generalization capability and robustness,
but also the trained images can maintain better edge contours and texture
features.
