The development of consumer depth cameras makes it possible to acquire depth information of a scene in real-time. However, low resolution and low quality of a depth map has greatly constrained its applications. In this paper, we propose a novel framework for single depth map super-resolution, which considers local and non-local information jointly in the depth map. For the non-local constraint, group-based sparse representation is used to explore non-local self-similarity in the depth map. For the local constraint, a multi-directional gradient-guided regularization is proposed to describe the gradient of the depth map with spatially varying orientations. The former constraint contains the visual artifacts effectively, while the latter restores sharp edge and fine structure. Finally, the two complementary regularizers are jointly casted into a unified optimization framework, where a split Bregman-based technique is developed to tackle the optimization problem. Both quantitative and qualitative evaluations indicate that the proposed method can obtain better reconstruction performance compared with state-of-the-art methods.

消费级深度相机的发展使得实时获取场景的深度信息成为可能。然而，深度图的低分辨率和低质量极大地限制了其应用。在本文中，我们提出了一种新的单深度图超分辨率框架，它在深度图中联合考虑了局部和非局部信息。对于非局部约束，使用基于组的稀疏表示来探索深度图中的非局部自相似性。对于局部约束，提出了多方向梯度引导正则化来描述具有空间变化方向的深度图的梯度。前者约束有效地包含了视觉伪影，而后者则恢复了锐利的边缘和精细的结构。最后，两个互补的正则化器被共同铸造成一个统一的优化框架，其中开发了一种基于拆分 Bregman 的技术来解决优化问题。定量和定性评估表明，与最先进的方法相比，所提出的方法可以获得更好的重建性能。