Depth image super-resolution algorithm based on structural features and non-local means  

作　　者：WANG Jing ZHANG Wei-zhong HUANG Bao-xiang YANG Huan 王靖;张维忠;黄宝香;杨环(College of Computer Science and Technology,Qingdao University)

机构地区：College of Computer Science and Technology,Qingdao University,Qingdao 266071,China

出　　处：《Optoelectronics Letters》2018年第5期391-395,共5页光电子快报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.61602269);Shandong Province Science and Technology Development Project(No.2014GGX101048);China Postdoctoral Science Foundation(No.2015M571993);Shandong Provincial Natural Science Foundation(No.ZR2017MD004)

摘　　要：The resolution and quality of the depth map captured by depth cameras are limited due to sensor hardware limitations, which becomes a roadblock for further computer vision applications. In order to solve this problem, we propose a new method to enhance low-resolution depth maps using high-resolution color images. The structural-aware term is introduced because of the availability of structural information in color images and the assumption of identical structural features within local neighborhoods of color images and depth images captured from the same scene. We integrate the structural-aware term with color similarity and depth similarity within local neighborhoods to design a local weighting filter based on structural features. To use non-local self-similarity of images, the local weighting filter is combined with the concept of non-local means, and then a non-local weighting filter based on structural features is designed. Some experimental results show that super-resolution depth image can be reconstructed well by the process of the non-local filter and the local filter based on structural features. The proposed method can reconstruct much better high-resolution depth images compared with previously reported methods.

关 键 词：深度图象 非局部 工具 集成结构 特征设计 算法 低分辨率 高分辨率 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] O175.26[自动化与计算机技术—计算机科学与技术]