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Depth inpainting scheme based on edge guided non local means

机译:基于边缘引导非局部均值的深度修补方案

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The acquisition process of depth image usually produces noise and holes. This kind of defect can reduce the quality of depth image especially the existence of small or large holes which cause great loss of depth information. In this research, the depth inpainting scheme based on edge guided non local means is proposed to restore the missing depth information. Non local means (NL-means) uses similar patches within search window to recover the missing depth pixel by averaging the weighted Euclidean distance from the similar patches. The inpainting scheme iterates through boundaries of missing depth pixels. Depth image edges will act as guidance to limit the search region by using Breadth First Search (BFS). The distance calculation is performed on valid depth pixels and ignore the missing depth parts from both patches to avoid false depth information. The experiment on the depth image dataset shows that the proposed scheme can fill small to large holes on depth image with low MSE value.
机译:深度图像的采集过程通常会产生噪声和孔洞。这种缺陷会降低深度图像的质量,特别是存在会导致深度信息大量丢失的小孔或大孔。在研究中,提出了一种基于边缘引导非局部手段的深度修复方案,以恢复缺失的深度信息。非局部均值(NL-means)在搜索窗口中使用相似的补丁,通过平均来自相似补丁的加权欧几里得距离来恢复丢失的深度像素。修复方案通过缺少深度像素的边界进行迭代。深度图像边缘将用作使用广度优先搜索(BFS)来限制搜索区域的指导。距离计算是在有效深度像素上执行的,并且忽略了两个补丁中缺失的深度部分,以避免错误的深度信息。在深度图像数据集上的实验表明,该方案可以在低MSE值的深度图像上填充小到大的空洞。

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