Three-dimensional video inpainting using gradient fusion and clustering
Yili Lai, Xianghong Tang, Xingxin Lou · 2017
For the foreground and background segmentation in Three-Dimensional(3D) video inpainting, the inaccuracy of the foreground extraction will easily affect the quality of the repaired image. In order to solve this problem, this paper combines watershed algorithm with marker for foreground extraction, making full use of the structural information of the depth image. In order to enhance the ability of distinguishing the foreground object, introducing K-means clustering to mark it in the gradient image. The experimental results show that the improved algorithm overcomes the defect of original watershed algorithm in image segmentation, which is likely to occur the over-segmentation phenomenon, and it also can completely extract the texture information of the foreground object, making the repaired image have better visual effect, and the peak signal-to-noise ratio(PSNR) is increased by 1 to 3 dB, compared to the other algorithm.