Generalized Video Deblurring for Dynamic Scenes (1507.02438v1)

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.

Generalized Video Deblurring for Dynamic Scenes

The paper "Generalized Video Deblurring for Dynamic Scenes" by Tae Hyun Kim and Kyoung Mu Lee proposes a novel approach to address the challenge of deblurring videos captured in dynamic environments. Unlike traditional methods which rely on the assumption that scenes are static, the authors present a methodology capable of managing the complexities and variations inherent to dynamic scenes. These variations often include camera shake, moving objects, and depth variation, each contributing to locally varying blur patterns that are difficult to model with conventional techniques.

The authors introduce a video deblurring method that utilizes bidirectional optical flows to approximate pixel-wise blur kernels. This approach enables the handling of general blurs caused by various sources in dynamic scenarios. The foundation of the method lies in a single energy model designed to jointly estimate optical flows and latent frames. This model is optimized using a framework equipped with efficient solvers aimed at minimizing the energy function. Importantly, the integration of temporal coherence into optical flow estimation enhances the handling of sharp transitions in motion, which is a critical aspect in dynamic scenes.

Key numerical results in the paper demonstrate substantial improvements over existing methods both in terms of deblurring performance and optical flow estimation accuracy. For example, comparisons with state-of-the-art methods illustrate the superiority of the proposed approach in real and challenging videos, where conventional models often fail.

The implications of this research are noteworthy. Practically, the method offers a significant advancement in video quality enhancement technologies, which are valuable in consumer electronics and professional film production. Theoretically, the paper contributes to the understanding of motion dynamics and their reconstruction, paving the way for further developments in computational photography and computer vision.

While the paper effectively demonstrates the robustness and applicability of the approach in various scenarios, future work may explore extensions to handle even more complex scenes or leverage the advancements in neural networks to potentially improve the computational efficiency and accuracy further. Additionally, examining scenarios with longer exposure times and incorporating depth information directly when available are promising avenues for research and enhancement of the proposed deblurring methodology in dynamic scenes.