Abstract
Deep learning-based image restoration has achieved significant success. However, when addressing real-world degradations, model performance is limited by the quality of ground-truth images in datasets due to practical constraints in data acquisition. To address this limitation, we propose a novel framework that enhances existing ground truth images to provide higher-quality supervision for real-world restoration. Our framework generates perceptually enhanced ground truth images using super-resolution by incorporating adaptive frequency masks, which are learned by a conditional frequency mask generator. These masks guide the optimal fusion of frequency components from the original ground truth and its super-resolved variants, yielding enhanced ground truth images. This frequency-domain mixup preserves the semantic consistency of the original content while selectively enriching perceptual details, preventing hallucinated artifacts that could compromise fidelity. The enhanced ground truth images are used to train a lightweight output refinement network that can be seamlessly integrated with existing restoration models. Extensive experiments demonstrate that our approach consistently improves the quality of restored images. We further validate the effectiveness of both supervision enhancement and output refinement through user studies. Code is available at this https URL.
Abstract (translated)
基于深度学习的图像恢复技术已经取得了显著的成功。然而，在处理现实世界中的退化问题时，由于数据采集的实际限制，模型性能受到训练集中真实地面图像质量的影响而受限。为了解决这一局限性，我们提出了一种新颖框架，通过增强现有的地面真相图像来提供更高质量的真实世界图像恢复指导。 我们的框架使用超分辨率技术生成感知上增强的地面真相图像，并结合自适应频率掩码，这些掩码由条件频率掩码生成器学习得到。这些掩码引导原始地面真相和其超级解析变体之间的频域成分的最佳融合，从而产生增强的地面真相图像。这种频域混合保持了原始内容的语义一致性，同时选择性地丰富感知细节，防止可能损害真实性的幻觉伪影。 我们利用生成的增强地面真相图像来训练一个轻量级的输出精炼网络，并可以无缝集成到现有的恢复模型中。大量的实验表明，我们的方法能够持续提高恢复图像的质量。通过用户研究进一步验证了监督提升和输出精炼的有效性。代码可在提供的链接处获取。
URL
https://arxiv.org/abs/2512.03932
