用扩散模型实现可调控的模糊图像恢复，无需知道模糊类型。
Controllable blind deblurring with diffusion models
- 基于扩散模型，通过模糊度量控制恢复强度。
- 微调扩散先验比固定主干效果更好，细节更真实。
- 适合需要精细控制恢复程度的专业摄影场景。
相机成像过程受光学系统、传感器或低级处理步骤影响，导致多种退化。本文针对专业摄影中的盲去模糊问题：在未知各向同性模糊核的情况下还原图像。对于此类逆问题（高频信息丢失），生成模型难以同时保证细节的真实感与输入一致性。我们提出SuperSharpen，一种基于扩散模型的盲去模糊方法，通过模糊度量实现恢复强度的显式控制。比较了两种条件策略：冻结主干的ControlNet式适配器，以及对扩散先验进行完整微调。实验表明，微调方案在保持更高保真度的同时减少幻觉细节。在合成与真实世界模糊数据上验证，本方法显著提升感知质量，并实现可控的恢复强度。
原文摘要 · Abstract (English)
Image acquisition with a camera involves several degradations due to the optical system, sensor, or low-level processing steps. We address blind deblurring in professional photography: we aim to invert unknown isotropic blur without knowledge of the degradation kernel. For such inverse problems,where some high-frequency information is lost, it is challenging to use generative models to produce details that are both photo-realistic and faithful to the input. We propose SuperSharpen, a diffusion-based blind deblurring method offering explicit control over restoration strength through a blur measure. We compare two conditioning strategies: a ControlNet-style adapter on a frozen backbone, and full finetuning of the diffusion prior. Our experiments show that finetuning achieves better fidelity with fewer hallucinated details. We validate our approach on synthetic and real-world blur, demonstrating improved perceptual quality and controllable restoration strength.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。