Generalized Video Deblurring for Dynamic Scenes

Abstract

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 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.

CVPR 2015 oral

Cited by in corpus (9)

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• Reblur2Deblur: Deblurring Videos via Self-Supervised Learning
• Occlusion-Aware Video Deblurring with a New Layered Blur Model
• Video Deblurring via Semantic Segmentation and Pixel-Wise Non-Linear Kernel
• Stereo Video Deblurring