目的 越来越多的应用都依赖于对真实场景深度图像的准确且快速的观测和分析。飞行时间相机可以实时获取场景的深度图像，但是由于硬件条件的限制，采集的深度图像分辨率较低，无法满足实际应用的需要。为此提出一种结合同场景彩色图像通过构造自适应权值滤波器对深度图像进行超分辨率重建的方法。方法 充分发掘深度图像的非局部以及局部自相似性先验约束，结合同场景的高分辨率彩色图像构造非局部及局部的自适应权值滤波算法对深度图像进行超分辨率重建。具体来说，首先利用非局部滤波算法来有效避免重建结果的振铃效应，然后利用局部滤波算法进一步提升重建的深度图像质量。结果 实验结果表明，无论在客观指标还是视觉效果上，基于自适应权值滤波的超分辨率重建算法较其他算法都可以得到更好的结果，尤其当初始的低分辨率深度图像质量较差的情况下，本文方法的优势更加明显，峰值信噪比可以得到1dB的提升。结论 结合非局部和局部自相似性先验约束，结合同场景的高分辨率彩色图像构造的自适应权值滤波算法，较其他算法可以得到更理想的结果。

Objective 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. Method 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. Result 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. Conclusion 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.