gSMOOTH: A Gradient based Spatial and Temporal Method of Depth Image Enhancement
gSMOOTH: A Gradient based Spatial and Temporal Method of Depth Image Enhancement
复制标题
DOI：
10.1145/3208159.3208166
复制
发表时间：
2018-06
期刊：
影响因子：
3.6
通讯作者：
A. T. Islam;M. Luboschik;Anton Jirka;O. Staadt
中科院分区：
文献类型：
--
作者：
A. T. Islam;M. Luboschik;Anton Jirka;O. Staadt
来自RGB-D相机的深度图像包含大量的伪影，例如孔和闪烁。此外，对于连续帧中的快速移动对象，我们在深度图像上感知到重影伪影。因此，深度图像的差质量限制了它们在各种应用中的使用。在这里，我们提出了一个基于梯度的空间和时间的深度增强方法（gSMOOTH）使用最小平方中值，它处理这些文物。对于帧序列上的每个深度像素，我们寻找无效或不稳定或急剧变化的深度值，并使用我们的方法将这些值替换为稳定且更可行的深度值。我们的方法去除了重影伪影和闪烁，并在真实的时间中显著地衰减了时间噪声的量。我们用自己的数据集和参考数据集进行实验，并根据参考方法评估我们的方法。实验结果表明，静态和动态场景的改进。
The depth images from RGB-D cameras contain a substantial amount of artifacts such as holes and flickering. Moreover, for fast moving objects in successive frames, we perceive ghosting artifacts on the depth images. Hence, the poor quality of the depth images limits them to be used in various applications. Here, we propose a gradient based spatial and temporal method of depth enhancement (gSMOOTH) using least median of squares, which deals with these artifacts. For each depth pixel over a sequence of frames, we look for invalid or unstable or drastically changed depth values and use our approach to replace those values with stable and more feasible depth values. Our approach removes the ghosting artifacts and flickering, and attenuates the amount of temporal noise significantly in real time. We conduct experiments with our own- and reference datasets and evaluate our method against reference methods. Experimental results show improvements for both static and dynamic scenes.