基于物理模型的模糊学习框架，提升成像系统模糊估计精度与通用性。
A Physics-Informed Blur Learning Framework for Imaging Systems
- 提出波前基模糊模型，降低优化复杂度，提升估计准确率。
- 无需透镜参数先验知识，实测与仿真均显著改善图像质量。
- 适合需要高精度模糊建模的成像系统，如显微、遥感等场景。
准确的模糊估计对各类成像应用至关重要。模糊通常由点扩散函数（PSF）表示。本文提出一种面向成像系统的物理信息引导式PSF学习框架，包含简单标定与学习过程，兼具高精度与强泛化能力。受塞德尔PSF模型启发，我们识别其在优化中的局限性，提出新型波前基PSF模型及优化策略，有效降低优化复杂度并提升估计精度。该模型不依赖镜头参数，无需事先了解镜头信息。通过去模糊任务验证，将所估PSF用于训练前沿去模糊算法，相比最近的退化迁移法和快速两步法，在仿真与真实图像上均实现显著的视觉质量提升。
原文摘要 · Abstract (English)
Accurate blur estimation is essential for high-performance imaging across various applications. Blur is typically represented by the point spread function (PSF). In this paper, we propose a physics-informed PSF learning framework for imaging systems, consisting of a simple calibration followed by a learning process. Our framework could achieve both high accuracy and universal applicability. Inspired by the Seidel PSF model for representing spatially varying PSF, we identify its limitations in optimization and introduce a novel wavefront-based PSF model accompanied by an optimization strategy, both reducing optimization complexity and improving estimation accuracy. Moreover, our wavefront-based PSF model is independent of lens parameters, eliminate the need for prior knowledge of the lens. To validate our approach, we compare it with recent PSF estimation methods (Degradation Transfer and Fast Two-step) through a deblurring task, where all the estimated PSFs are used to train state-of-the-art deblurring algorithms. Our approach demonstrates improvements in image quality in simulation and also showcases noticeable visual quality improvements on real captured images.
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