Generalized Video Deblurring for Dynamic Scenes
Tae Hyun Kim
Kyoung Mu Lee
AbstractSeveral state-of-the-art video deblurring methods are based on a strong assumption that the captured scenes are static. These methods fail to deblur blurry videos in dynamic scenes. We propose a video deblurring method to deal with general blurs inherent in dynamic scenes, contrary to other methods. To handle locally varying and general blurs caused by various sources, such as camera shake, moving objects, and depth variation in a scene, we approximate pixel-wise kernel with bidirectional optical flows. Therefore, we propose a single energy model that simultaneously estimates optical flows and latent frames to solve our deblurring problem. We also provide a framework and efficient solvers to optimize the energy model. By minimizing the proposed energy function, we achieve significant improvements in removing blurs and estimating accurate optical flows in blurry frames. Extensive experimental results demonstrate the superiority of the proposed method in real and challenging videos that state-of-the-art methods fail in either deblurring or optical flow estimation.
1. Tae Hyun Kim and Kyoung Mu Lee, "Generalized Video Deblurring for Dynamic Scenes", Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
2. Tae Hyun Kim and Kyoung Mu Lee, "Segmentation-free Dynamic Scene Deblurring", Conference on Computer Vision and Pattern Recognition (CVPR), 2014.