Diffusion models (DM) have achieved remarkable promise in image super-resolution (SR). However, most of them are tailored to solving non-blind inverse problems with fixed known degradation settings, limiting their adaptability to real-world applications that involve complex unknown degradations. In this work, we propose BlindDiff, a DM-based blind SR method to tackle the blind degradation settings in SISR. BlindDiff seamlessly integrates the MAP-based optimization into DMs, which constructs a joint distribution of the low-resolution (LR) observation, high-resolution (HR) data, and degradation kernels for the data and kernel priors, and solves the blind SR problem by unfolding MAP approach along with the reverse process. Unlike most DMs, BlindDiff firstly presents a modulated conditional transformer (MCFormer) that is pre-trained with noise and kernel constraints, further serving as a posterior sampler to provide both priors simultaneously. Then, we plug a simple yet effective kernel-aware gradient term between adjacent sampling iterations that guides the diffusion model to learn degradation consistency knowledge. This also enables to joint refine the degradation model as well as HR images by observing the previous denoised sample. With the MAP-based reverse diffusion process, we show that BlindDiff advocates alternate optimization for blur kernel estimation and HR image restoration in a mutual reinforcing manner. Experiments on both synthetic and real-world datasets show that BlindDiff achieves the state-of-the-art performance with significant model complexity reduction compared to recent DM-based methods. Code will be available at \url{https://github.com/lifengcs/BlindDiff}

翻译：扩散模型（DM）在图像超分辨率（SR）领域展现出显著潜力，但现有方法大多针对已知固定退化设置的非盲逆问题设计，难以适应包含复杂未知退化的实际应用。本文提出基于DM的盲SR方法BlindDiff，以应对SISR中的盲退化设置。BlindDiff将基于最大后验概率（MAP）的优化无缝融入扩散模型，通过构建低分辨率观测、高分辨率数据与退化核的联合分布来获取数据先验与核先验，并沿着反向过程展开MAP方法求解盲SR问题。与多数DM不同，BlindDiff首先提出一种调制条件Transformer（MCFormer）——该模型通过噪声与核约束进行预训练，可作为后验采样器同时提供两种先验。随后，我们在相邻采样迭代间嵌入简洁有效的核感知梯度项，引导扩散模型学习退化一致性知识，从而通过观测前一步去噪样本联合优化退化模型与高分辨率图像。基于MAP反向扩散过程，BlindDiff以相互强化的交替优化方式实现模糊核估计与高分辨率图像恢复。在合成与真实数据集上的实验表明，与近期基于DM的方法相比，BlindDiff在显著降低模型复杂度的同时取得了最先进性能。代码将发布于\url{https://github.com/lifengcs/BlindDiff}