Blind image deconvolution is a challenging ill-posed inverse problem, where both the latent sharp image and the blur kernel are unknown. Traditional methods often rely on handcrafted priors, while modern deep learning approaches typically require extensive pre-training on large external datasets, limiting their adaptability to real-world scenarios. In this work, we propose DeblurSDI, a zero-shot, self-supervised framework based on self-diffusion (SDI) that requires no prior training. DeblurSDI formulates blind deconvolution as an iterative reverse self-diffusion process that starts from pure noise and progressively refines the solution. At each step, two randomly-initialized neural networks are optimized continuously to refine the sharp image and the blur kernel. The optimization is guided by an objective function combining data consistency with a sparsity-promoting L1-norm for the kernel. A key innovation is our noise scheduling mechanism, which stabilizes the optimization and provides remarkable robustness to variations in blur kernel size. These allow DeblurSDI to dynamically learn an instance-specific prior tailored to the input image. Extensive experiments demonstrate that DeblurSDI consistently achieves superior performance, recovering sharp images and accurate kernels even in highly degraded scenarios.

翻译：盲图像反卷积是一个具有挑战性的病态逆问题，其中潜在的清晰图像和模糊核均未知。传统方法通常依赖于手工设计的先验，而现代深度学习方法通常需要在大型外部数据集上进行大量预训练，这限制了其在真实场景中的适应性。在本工作中，我们提出了DeblurSDI，一种基于自扩散（SDI）的零样本自监督框架，无需预先训练。DeblurSDI将盲反卷积表述为一个迭代的反向自扩散过程，该过程从纯噪声开始，逐步优化解。在每一步中，两个随机初始化的神经网络被持续优化以细化清晰图像和模糊核。优化过程由结合数据一致性和促进模糊核稀疏性的L1范数的目标函数引导。一个关键创新是我们的噪声调度机制，该机制稳定了优化过程，并对模糊核尺寸的变化表现出显著的鲁棒性。这些特性使得DeblurSDI能够动态学习针对输入图像量身定制的实例特定先验。大量实验表明，DeblurSDI始终能实现卓越的性能，即使在高度退化场景下也能恢复出清晰图像和准确的模糊核。