BlurDM：一种用于图像去模糊的模糊扩散模型

摘要

扩散模型在动态场景去模糊领域展现出巨大潜力，但现有研究往往未能充分利用模糊过程在扩散模型中的内在特性，限制了其潜力的充分发挥。为此，我们提出模糊扩散模型（BlurDM），将模糊形成过程无缝集成到扩散框架中实现图像去模糊。基于运动模糊源于连续曝光的观察，BlurDM通过双扩散前向方案隐式建模模糊形成过程，使噪声和模糊共同作用于清晰图像。在反向生成过程中，我们推导出双重去噪与去模糊的数学表述，使得BlurDM能够以模糊图像为条件输入的高斯噪声为基础，同步执行去噪与去模糊操作以重建清晰图像。此外，为高效整合BlurDM至去模糊网络，我们在隐空间执行BlurDM运算，构建出灵活的先验生成网络用于去模糊任务。大量实验表明，BlurDM在四个基准数据集上能显著且持续地提升现有去模糊方法的性能。源代码已发布于https://github.com/Jin-Ting-He/BlurDM。

English

Diffusion models show promise for dynamic scene deblurring; however, existing studies often fail to leverage the intrinsic nature of the blurring process within diffusion models, limiting their full potential. To address it, we present a Blur Diffusion Model (BlurDM), which seamlessly integrates the blur formation process into diffusion for image deblurring. Observing that motion blur stems from continuous exposure, BlurDM implicitly models the blur formation process through a dual-diffusion forward scheme, diffusing both noise and blur onto a sharp image. During the reverse generation process, we derive a dual denoising and deblurring formulation, enabling BlurDM to recover the sharp image by simultaneously denoising and deblurring, given pure Gaussian noise conditioned on the blurred image as input. Additionally, to efficiently integrate BlurDM into deblurring networks, we perform BlurDM in the latent space, forming a flexible prior generation network for deblurring. Extensive experiments demonstrate that BlurDM significantly and consistently enhances existing deblurring methods on four benchmark datasets. The source code is available at https://github.com/Jin-Ting-He/BlurDM.

BlurDM：一种用于图像去模糊的模糊扩散模型

BlurDM: A Blur Diffusion Model for Image Deblurring

摘要

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