IEEE Trans Cybern. 2018 Jan;48(1):371-384. doi: 10.1109/TCYB.2016.2637661. Epub 2017 Jan 24.
Compared to the color images, their associated depth images captured by the RGB-D sensors are typically with lower resolution. The task of depth map super-resolution (SR) aims at increasing the resolution of the range data by utilizing the high-resolution (HR) color image, while the details of the depth information are to be properly preserved. In this paper, we present a joint trilateral filtering (JTF) algorithm for depth image SR. The proposed JTF first observes context information from the HR color image. In addition to the extracted spatial and range information of local pixels, our JTF further integrates local gradient information of the depth image, which allows the prediction and refinement of HR depth image outputs without artifacts like textural copies or edge discontinuities. Quantitative and qualitative experimental results demonstrate the effectiveness and robustness of our approach over prior depth map upsampling works.
与彩色图像相比，RGB-D 传感器采集的相关深度图像通常分辨率较低。深度图超分辨率 (SR) 的任务旨在通过利用高分辨率 (HR) 彩色图像来提高范围数据的分辨率，同时适当保留深度信息的细节。在本文中，我们提出了一种用于深度图像 SR 的联合三边滤波 (JTF) 算法。所提出的 JTF 首先从 HR 彩色图像中观察上下文信息。除了提取局部像素的空间和范围信息外，我们的 JTF 还进一步集成了深度图像的局部梯度信息，这允许对 HR 深度图像输出进行预测和细化，而不会出现纹理复制或边缘不连续等伪影。定量和定性实验结果表明，我们的方法在先前的深度图上采样工作中具有有效性和鲁棒性。
