  Image Restoration has seen remarkable progress in recent years. Many
generative models have been adapted to tackle the known restoration cases of
images. However, the interest in benefiting from the frequency domain is not
well explored despite its major factor in these particular cases of image
synthesis. In this study, we propose the Guided Frequency Loss (GFL), which
helps the model to learn in a balanced way the image's frequency content
alongside the spatial content. It aggregates three major components that work
in parallel to enhance learning efficiency; a Charbonnier component, a
Laplacian Pyramid component, and a Gradual Frequency component. We tested GFL
on the Super Resolution and the Denoising tasks. We used three different
datasets and three different architectures for each of them. We found that the
GFL loss improved the PSNR metric in most implemented experiments. Also, it
improved the training of the Super Resolution models in both SwinIR and SRGAN.
In addition, the utility of the GFL loss increased better on constrained data
due to the less stochasticity in the high frequencies' components among
samples.

Computer Vision and Pattern Recognition;Image and Video Processing