Advanced Digital Sciences Center, Singapore.
IEEE Trans Image Process. 2012 Mar;21(3):1176-90. doi: 10.1109/TIP.2011.2163164. Epub 2011 Jul 29.
This paper presents a novel approach for depth video enhancement. Given a high-resolution color video and its corresponding low-quality depth video, we improve the quality of the depth video by increasing its resolution and suppressing noise. For that, a weighted mode filtering method is proposed based on a joint histogram. When the histogram is generated, the weight based on color similarity between reference and neighboring pixels on the color image is computed and then used for counting each bin on the joint histogram of the depth map. A final solution is determined by seeking a global mode on the histogram. We show that the proposed method provides the optimal solution with respect to L(1) norm minimization. For temporally consistent estimate on depth video, we extend this method into temporally neighboring frames. Simple optical flow estimation and patch similarity measure are used for obtaining the high-quality depth video in an efficient manner. Experimental results show that the proposed method has outstanding performance and is very efficient, compared with existing methods. We also show that the temporally consistent enhancement of depth video addresses a flickering problem and improves the accuracy of depth video.
本文提出了一种新的深度视频增强方法。给定高分辨率彩色视频及其对应的低质量深度视频，我们通过提高分辨率和抑制噪声来提高深度视频的质量。为此，提出了一种基于联合直方图的加权模式滤波方法。在生成直方图时，计算基于彩色图像上参考像素和相邻像素之间颜色相似性的权重，并用于对深度图的联合直方图中的每个 bin 进行计数。通过在直方图上寻找全局模式来确定最终的解决方案。我们表明，所提出的方法提供了关于 L(1)范数最小化的最优解。为了在深度视频上进行时间一致的估计，我们将该方法扩展到时间相邻的帧。简单的光流估计和补丁相似性度量用于以有效的方式获得高质量的深度视频。实验结果表明，与现有方法相比，所提出的方法具有出色的性能和非常高的效率。我们还表明，深度视频的时间一致增强解决了闪烁问题并提高了深度视频的准确性。
