Several 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 dy namic scenes. We propose a video deblurring method to deal with general blurs inherent in dynamic scenes, con trary to other methods. To handle locally varying and gen eral blurs caused by various sources, such as camera shake, moving objects, and depth variation in a scene, we ap proximate pixel-wise kernel with bidirectional opticalflows. Therefore, we propose a single energy model that simulta neously 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 minimiz ing the proposed energy junction, we achieve significant im provements in removing blurs and estimating accurate op tical 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.
Instead of assuming the captured scenes are static, REF first proposes an effective general video deblurring method for dynamic scenes.
Generalized video deblurring for dynamic scenes
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Science