Video Deblurring by Segmentation from Motion

It is difficult to carry out large related motion blurs and keep both spatial and temporal coherent in deblurring results using existing video deblurring methods. In this paper, we propose a segmentation-based video deblurring method to deal with such large related motion blurs. Since the sharpness is varying on video frames, blurry frames can be restored by sharp patches. Our method firstly divides a blurring frame into several motion-based regions using optical flow between the blurring frame and its adjacent sharp frame. Then, the blur function is estimated for each region to blur sharp neighbor frame. Finally, we search the nearest patches from blurred sharp frames and restore the final results using region-based synthesis. To improve the efficiency, the method is implemented based on GPU acceleration. Experimental results demonstrate that our segmentation-based video deblurring method can remove blurry artifact effectively on real video sequences.