IEEE Trans Pattern Anal Mach Intell. 2011 Aug;33(8):1603-18. doi: 10.1109/TPAMI.2010.222. Epub 2010 Dec 23.
This paper addresses how to model and correct image blur that arises when a camera undergoes ego motion while observing a distant scene. In particular, we discuss how the blurred image can be modeled as an integration of the clear scene under a sequence of planar projective transformations (i.e., homographies) that describe the camera's path. This projective motion path blur model is more effective at modeling the spatially varying motion blur exhibited by ego motion than conventional methods based on space-invariant blur kernels. To correct the blurred image, we describe how to modify the Richardson-Lucy (RL) algorithm to incorporate this new blur model. In addition, we show that our projective motion RL algorithm can incorporate state-of-the-art regularization priors to improve the deblurred results. The projective motion path blur model, along with the modified RL algorithm, is detailed, together with experimental results demonstrating its overall effectiveness. Statistical analysis on the algorithm's convergence properties and robustness to noise is also provided.
本文讨论了如何对相机在观测远距离场景时发生的自身运动引起的图像模糊进行建模和校正。具体来说，我们讨论了如何将模糊图像建模为清晰场景在一系列平面投影变换（即单应变换）下的积分，这些变换描述了相机的路径。与基于空间不变模糊核的传统方法相比，这种投影运动路径模糊模型更有效地模拟了自身运动表现出的空间变化运动模糊。为了校正模糊图像，我们描述了如何修改 Richardson-Lucy（RL）算法以纳入这种新的模糊模型。此外，我们还表明，我们的投影运动 RL 算法可以结合最新的正则化先验来改善去模糊结果。详细介绍了投影运动路径模糊模型和修改后的 RL 算法，并展示了其实验结果，证明了其整体有效性。还提供了对算法收敛特性和对噪声鲁棒性的统计分析。
