PRISM用测量条件扩散模型解决未知退化图像复原问题
PRISM: Probabilistic and Robust Inverse Solver with Measurement-Conditioned Diffusion Prior for Blind Inverse Problems
- 将测量值融入扩散模型，构建条件先验
- 在盲去模糊任务中同时提升图像与模糊核恢复精度
- 适合处理退化过程未知的成像逆问题
扩散模型现被广泛用于计算成像中的逆问题求解。然而，现有基于扩散的逆求解器大多需要完全已知的前向算子才能使用。本文提出一种新型概率且鲁棒的逆求解器PRISM，通过引入测量条件扩散先验，有效解决盲逆问题。PRISM在理论上严谨的后验采样框架中融合了强大的测量条件扩散模型，技术上实现突破。在盲图像去模糊任务上的实验验证了该方法的有效性，结果表明其在图像和模糊核恢复方面均优于当前最优基线方法。
原文摘要 · Abstract (English)
Diffusion models are now commonly used to solve inverse problems in computational imaging. However, most diffusion-based inverse solvers require complete knowledge of the forward operator to be used. In this work, we introduce a novel probabilistic and robust inverse solver with measurement-conditioned diffusion prior (PRISM) to effectively address blind inverse problems. PRISM offers a technical advancement over current methods by incorporating a powerful measurement-conditioned diffusion model into a theoretically principled posterior sampling scheme. Experiments on blind image deblurring validate the effectiveness of the proposed method, demonstrating the superior performance of PRISM over state-of-the-art baselines in both image and blur kernel recovery.
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