The ability to capture depth information of static real-world objects has reached increased importance in many fields of application

such as manufacturing and prototyping as well as the design of virtual worlds for movies and games. The use of time-of-flight camera to obtain the scene depth map is very convenient

but given the limitations of the hardware

the resolution of the depth map is very low and cannot meet the actual needs. How to improve the resolution of the depth image is an interesting topic. To overcome this problem

we propose a novel method for solving the depth map super-resolution problem. Given a low-resolution depth map as input

we recover a high-resolution depth map by using a registered high-resolution color image. Based on the benefits of non-local and local priors

we propose a novel adaptive weighting filter framework to solve this depth map super-resolution problem. Specifically

given that discontinuities in range and color tend to co-align

we formulate the non-local and local adaptive weighting filters based on the raw depth map and the features of high-resolution color images. With this non-local adaptive weighting filter

our algorithm can well prevent the depth super-resolution result from the jagged effect and is more robust to different initial depth input. Then

our local adaptive weighting filter can further improve the quality of the reconstructed depth results. Experiments demonstrate that our approach can obtain excellent high-resolution range images in terms of both spatial resolution and depth precision. The Peak Signal to Noise Ratio (PSNR) comparison experiments show that our method can reconstruct much better high-resolution range images compared with other state-of-the-art methods. Especially when the down-sample factor is larger

the performance of our algorithm is more obvious. In this paper

we present an adaptive weighting filter framework for the depth map super-resolution problem. Based on the mutual benefits between the raw depth map and the visual features of the color image

we formulate the super-resolution process as an adaptive weighting filter integrating non-local and local priors. It is experimentally shown that the proposed methods can produce sharper edges and more faithful details compared with other state-of-the-art approaches.