Accurate edge reconstruction is critical for depth map super resolution (SR). Therefore, many traditional SR methods utilize edge maps to guide depth SR. However, it is difficult to predict accurate edge maps from low resolution (LR) depth maps. In this paper, we propose a deep edge map guided depth SR method, which includes an edge prediction subnetwork and an SR subnetwork. The edge prediction subnetwork takes advantage of the hierarchical representation of color and depth images to produce accurate edge maps, which promote the performance of SR subnetwork. The SR subnetwork is a disentangling cascaded network to progressively upsample SR result, where every level is made up of a weight sharing module and an adaptive module. The weight sharing module extracts the general features in different levels, while the adaptive module transfers the general features to the specific features to adapt to different degraded inputs. Quantitative and qualitative evaluations on various datasets with different magnification factors demonstrate the effectiveness and promising performance of the proposed method. In addition, we construct a benchmark dataset captured by Kinect-v2 to facilitate research on real-world depth map SR.

精确的边缘重建对于深度图超分辨率（SR）至关重要。因此，许多传统的SR方法利用边缘图来指导深度SR。但是，很难从低分辨率（LR）深度图预测准确的边缘图。在本文中，我们提出了一种深边缘地图引导深度SR方法，该方法包括边缘预测子网和SR子网。边缘预测子网利用彩色和深度图像的分层表示来生成准确的边缘图，从而提高SR子网的性能。SR子网是一个解缠结的级联网络，用于逐步上采样SR结果，其中每个级别均由权重共享模块和自适应模块组成。权重共享模块提取不同级别的一般特征，自适应模块将通用特征转换为特定特征，以适应不同的降级输入。对具有不同放大倍数的各种数据集进行定量和定性评估，证明了该方法的有效性和前景。此外，我们构建了Kinect-v2捕获的基准数据集，以促进对现实世界深度图SR的研究。