超越真值：面向图像复原的增强监督
Beyond the Ground Truth: Enhanced Supervision for Image Restoration
把「提升真值质量」当作提升复原性能的杠杆：用条件频率掩码生成器做频域 mixup，把原真值与超分变体融合成感知增强真值，再训练一个可插拔的轻量输出精炼网络，可直接接到现有复原模型后面。
原文依据 generates perceptually enhanced ground truth images using super-resolution by incorporating adaptive frequency masks
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论基于深度学习的图像复原已取得显著成功。然而，在处理真实世界的退化时，受数据采集的实际限制，模型性能受限于数据集中真值图像的质量。为解决这一限制，我们提出了一个新框架，对现有真值图像进行增强，从而为真实世界复原提供更高质量的监督。我们的框架借助超分辨率生成感知增强的真值图像，其中引入自适应频率掩码，这些掩码由条件频率掩码生成器学习得到。这些掩码引导对原始真值及其超分辨率变体的频率分量进行最优融合，从而得到增强的真值图像。这种频域混合在选择性丰富感知细节的同时保持了原始内容的语义一致性，避免了可能损害保真度的幻觉伪影。增强的真值图像被用于训练一个轻量级输出精炼网络，该网络可以无缝集成到现有复原模型中。大量实验表明，我们的方法提升了复原图像的质量。我们进一步通过用户研究验证了监督增强与输出精炼两者的有效性。
英文原文摘要 1340 字符
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 improves the quality of restored images. We further validate the effectiveness of both supervision enhancement and output refinement through user studies.