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.

翻译：近年来，图像修复领域取得了显著进展。许多生成模型已被应用于处理已知的图像修复场景。然而，尽管频域分析在图像合成中具有重要作用，但利用频域信息的研究尚未得到充分探索。本研究提出引导式频率损失（Guided Frequency Loss, GFL），该损失函数帮助模型以平衡方式学习图像的频率内容与空间内容。它整合了三个并行工作的主要组件以提升学习效率：Charbonnier组件、拉普拉斯金字塔组件和渐进频率组件。我们在超分辨率和去噪任务上测试了GFL，针对每项任务使用三种不同数据集和三种架构。实验结果表明，GFL损失在多数实验中提升了PSNR指标，并改善了SwinIR和SRGAN超分辨率模型的训练效果。此外，由于高频成分在不同样本间的随机性降低，GFL损失在受限数据上的效用更为显著。