We present DiffBIR, a general restoration pipeline that could handle different blind image restoration tasks in a unified framework. DiffBIR decouples blind image restoration problem into two stages: 1) degradation removal: removing image-independent content; 2) information regeneration: generating the lost image content. Each stage is developed independently but they work seamlessly in a cascaded manner. In the first stage, we use restoration modules to remove degradations and obtain high-fidelity restored results. For the second stage, we propose IRControlNet that leverages the generative ability of latent diffusion models to generate realistic details. Specifically, IRControlNet is trained based on specially produced condition images without distracting noisy content for stable generation performance. Moreover, we design a region-adaptive restoration guidance that can modify the denoising process during inference without model re-training, allowing users to balance realness and fidelity through a tunable guidance scale. Extensive experiments have demonstrated DiffBIR's superiority over state-of-the-art approaches for blind image super-resolution, blind face restoration and blind image denoising tasks on both synthetic and real-world datasets. The code is available at https://github.com/XPixelGroup/DiffBIR.

翻译：我们提出DiffBIR——一种通用修复流水线，能在统一框架中处理多种盲图像修复任务。DiffBIR将盲图像修复问题解耦为两阶段：1）退化去除：移除图像无关内容；2）信息再生：生成缺失图像内容。两阶段独立开发，但通过级联方式无缝协同。第一阶段使用修复模块去除退化以获得高保真恢复结果；第二阶段提出IRControlNet，利用潜在扩散模型的生成能力重建逼真细节。具体而言，IRControlNet基于特殊生成的条件图像进行训练，避免干扰性噪声内容以实现稳定生成性能。此外，我们设计区域自适应修复引导机制，可在不重新训练模型的情况下修改推理过程中的去噪进程，通过可调节引导尺度让用户平衡真实性与保真度。大量实验表明，在合成和真实数据集上的盲图像超分辨率、盲人脸修复和盲图像去噪任务中，DiffBIR均显著优于现有最佳方法。代码已在https://github.com/XPixelGroup/DiffBIR 开源。