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，一种统一的通用修复流程，能在同一框架下处理多种盲图像修复任务。DiffBIR将盲图像修复问题解耦为两个阶段：1)退化消除——去除与图像无关的内容；2)信息再生——生成丢失的图像内容。两个阶段独立开发，但通过级联方式无缝协同。第一阶段，我们使用修复模块消除退化并获取高保真修复结果；第二阶段，提出IRControlNet，利用潜在扩散模型的生成能力来产生逼真细节。具体而言，IRControlNet基于特制的条件图像进行训练，避免含噪内容干扰，从而实现稳定生成性能。此外，我们设计了区域自适应修复引导机制，无需重新训练模型即可在推理阶段修改去噪过程，使用户能通过可调节的引导尺度平衡真实性与保真度。大量实验表明，DiffBIR在盲图像超分辨率、盲人脸修复和盲图像去噪任务中，无论是合成数据集还是真实数据集，均优于现有最优方法。代码开源地址：https://github.com/XPixelGroup/DiffBIR。