PG-ControlNet：一种用于生成空间变化图像去模糊的物理引导ControlNet
PG-ControlNet: A Physics-Guided ControlNet for Generative Spatially Varying Image Deblurring
AI总结：
PG-ControlNet通过结合物理约束与生成模型，提升空间变化图像去模糊的物理准确性和感知真实感。
摘要：
空间变化图像去模糊仍然是一个根本上不明确的问题，尤其是在退化来自复杂混合的运动和其他形式的模糊以及显著噪声的情况下。最先进的基于学习的方法通常分为两种范式：基于模型的深度展开方法通过建模退化来强制物理约束，但往往会产生过度平滑、有伪影的纹理；生成模型则在感知质量上表现优异，但因物理约束薄弱而产生幻觉细节。在本文中，我们提出了一种新的框架，通过将强大的生成先验与显式、密集的物理约束相结合，独特地协调了这两种范式。我们不是过度简化退化场，而是将其建模为高维压缩内核的密集连续体，以确保微小的运动和其他退化模式的变化得到捕捉。我们利用这一丰富的描述字段来指导ControlNet架构，强烈引导扩散采样过程。广泛的实验表明，我们的方法有效弥合了物理准确性和感知现实之间的差距，在具有挑战性和严重模糊的场景中优于最先进的基于模型的方法以及生成基线。
英文摘要：
Spatially varying image deblurring remains a fundamentally ill-posed problem, especially when degradations arise from complex mixtures of motion and other forms of blur under significant noise. State-of-the-art learning-based approaches generally fall into two paradigms: model-based deep unrolling methods that enforce physical constraints by modeling the degradations, but often produce over-smoothed, artifact-laden textures, and generative models that achieve superior perceptual quality yet hallucinate details due to weak physical constraints. In this paper, we propose a novel framework that uniquely reconciles these paradigms by taming a powerful generative prior with explicit, dense physical constraints. Rather than oversimplifying the degradation field, we model it as a dense continuum of high-dimensional compressed kernels, ensuring that minute variations in motion and other degradation patterns are captured. We leverage this rich descriptor field to condition a ControlNet architecture, strongly guiding the diffusion sampling process. Extensive experiments demonstrate that our method effectively bridges the gap between physical accuracy and perceptual realism, outperforming state-of-the-art model-based methods as well as generative baselines in challenging, severely blurred scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
