  Image super-resolution (SR) with generative adversarial networks (GAN) has
achieved great success in restoring realistic details. However, it is notorious
that GAN-based SR models will inevitably produce unpleasant and undesirable
artifacts, especially in practical scenarios. Previous works typically suppress
artifacts with an extra loss penalty in the training phase. They only work for
in-distribution artifact types generated during training. When applied in
real-world scenarios, we observe that those improved methods still generate
obviously annoying artifacts during inference. In this paper, we analyze the
cause and characteristics of the GAN artifacts produced in unseen test data
without ground-truths. We then develop a novel method, namely, DeSRA, to Detect
and then Delete those SR Artifacts in practice. Specifically, we propose to
measure a relative local variance distance from MSE-SR results and GAN-SR
results, and locate the problematic areas based on the above distance and
semantic-aware thresholds. After detecting the artifact regions, we develop a
finetune procedure to improve GAN-based SR models with a few samples, so that
they can deal with similar types of artifacts in more unseen real data.
Equipped with our DeSRA, we can successfully eliminate artifacts from inference
and improve the ability of SR models to be applied in real-world scenarios. The
code will be available at https://github.com/TencentARC/DeSRA.
