Deep learning-based models for All-In-One Image Restoration (AIOR) have achieved significant advancements in recent years. However, their practical applicability is limited by poor generalization to samples outside the training distribution. This limitation arises primarily from insufficient diversity in degradation variations and scenes within existing datasets, resulting in inadequate representations of real-world scenarios. Additionally, capturing large-scale real-world paired data for degradations such as haze, low-light, and raindrops is often cumbersome and sometimes infeasible. In this paper, we leverage the generative capabilities of latent diffusion models to synthesize high-quality degraded images from their clean counterparts. Specifically, we introduce GenDeg, a degradation and intensity-aware conditional diffusion model capable of producing diverse degradation patterns on clean images. Using GenDeg, we synthesize over 550k samples across six degradation types: haze, rain, snow, motion blur, low-light, and raindrops. These generated samples are integrated with existing datasets to form the GenDS dataset, comprising over 750k samples. Our experiments reveal that image restoration models trained on the GenDS dataset exhibit significant improvements in out-of-distribution performance compared to those trained solely on existing datasets. Furthermore, we provide comprehensive analyses on the implications of diffusion model-based synthetic degradations for AIOR. The code will be made publicly available.

翻译：基于深度学习的全能图像恢复（AIOR）模型近年来取得了显著进展。然而，其在实际应用中的适用性因对训练分布外样本的泛化能力不足而受限。这一限制主要源于现有数据集中退化变化和场景的多样性不足，导致对真实世界场景的表征不充分。此外，针对雾霾、低光照和雨滴等退化类型，获取大规模的真实世界配对数据通常繁琐且有时不可行。本文利用潜在扩散模型的生成能力，从干净图像合成高质量的退化图像。具体而言，我们提出了GenDeg，一种退化和强度感知的条件扩散模型，能够在干净图像上生成多样化的退化模式。使用GenDeg，我们合成了超过55万张涵盖六种退化类型的样本：雾霾、雨、雪、运动模糊、低光照和雨滴。这些生成的样本与现有数据集整合，形成了包含超过75万样本的GenDS数据集。实验表明，在GenDS数据集上训练的图像恢复模型，相较于仅使用现有数据集训练的模型，在分布外性能上展现出显著提升。此外，我们全面分析了基于扩散模型的合成退化对AIOR的影响。代码将公开提供。