Lee Kyungjae, Ban Yuseok, Lee Sangyoun
Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea.
Sensors (Basel). 2017 Jul 1;17(7):1544. doi: 10.3390/s17071544.
Studies on depth images containing three-dimensional information have been performed for many practical applications. However, the depth images acquired from depth sensors have inherent problems, such as missing values and noisy boundaries. These problems significantly affect the performance of applications that use a depth image as their input. This paper describes a depth enhancement algorithm based on a combination of color and depth information. To fill depth holes and recover object shapes, asynchronous cellular automata with neighborhood distance maps are used. Image segmentation and a weighted linear combination of spatial filtering algorithms are applied to extract object regions and fill disocclusion in the object regions. Experimental results on both real-world and public datasets show that the proposed method enhances the quality of the depth image with low computational complexity, outperforming conventional methods on a number of metrics. Furthermore, to verify the performance of the proposed method, we present stereoscopic images generated by the enhanced depth image to illustrate the improvement in quality.
针对许多实际应用，人们已开展了对包含三维信息的深度图像的研究。然而，从深度传感器获取的深度图像存在诸如缺失值和边界噪声等固有问题。这些问题严重影响了将深度图像作为输入的应用的性能。本文描述了一种基于颜色和深度信息相结合的深度增强算法。为了填充深度空洞并恢复物体形状，使用了带有邻域距离图的异步细胞自动机。应用图像分割和空间滤波算法的加权线性组合来提取物体区域并填充物体区域中的遮挡。在真实世界数据集和公共数据集上的实验结果表明，该方法以低计算复杂度提高了深度图像的质量，在多个指标上优于传统方法。此外，为了验证所提方法的性能，我们展示了由增强后的深度图像生成的立体图像，以说明质量的提升。
