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Video Deblurring for Hand-held Cameras Using Patch-based Synthesis

机译:使用基于补丁的合成对手持摄像机进行视频去模糊

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Videos captured by hand-held cameras often contain significant camera shake, causing many frames to be blurry. Restoring shaky videos not only requires smoothing the camera motion and stabilizing the content, but also demands removing blur from video frames. However, video blur is hard to remove using existing single or multiple image deblurring techniques, as the blur kernel is both spatially and temporally varying. This paper presents a video deblurring method that can effectively restore sharp frames from blurry ones caused by camera shake. Our method is built upon the observation that due to the nature of camera shake, not all video frames are equally blurry. The same object may appear sharp on some frames while blurry on others. Our method detects sharp regions in the video, and uses them to restore blurry regions of the same content in nearby frames. Our method also ensures that the deblurred frames are both spatially and temporally coherent using patch-based synthesis. Experimental results show that our method can effectively remove complex video blur under the presence of moving objects and other outliers, which cannot be achieved using previous deconvolution-based approaches.
机译:手持式摄像机拍摄的视频通常会引起明显的摄像机抖动,从而导致许多帧变得模糊。恢复晃动的视频不仅需要平滑摄像机的运动并稳定内容,而且还需要消除视频帧中的模糊。但是,由于模糊内核在空间和时间上都在变化,因此使用现有的单个或多个图像去模糊技术很难消除视频模糊。本文提出了一种视频去模糊方法,该方法可以有效地恢复由相机抖动引起的模糊帧中的清晰帧。我们的方法基于以下观察结果:由于摄像机抖动的性质,并非所有视频帧都同样模糊。同一对象在某些帧上可能显得清晰,而在另一些帧上则可能模糊。我们的方法可以检测视频中的尖锐区域,并使用它们来恢复附近帧中相同内容的模糊区域。我们的方法还使用基于补丁的合成方法确保去模糊的帧在空间和时间上都是连贯的。实验结果表明,我们的方法可以在存在运动对象和其他异常值的情况下有效消除复杂的视频模糊,而这是以前基于反卷积的方法无法实现的。

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