Depth Map Super-Resolution Based on the Local Structural Features of Color Image
YANG Yu-Xiang1,WANG Zeng-Fu1,2
1. Department of Automation,University of Science and Technology of China,Hefei 230026 2. Institute of Intelligent Machines,Chinese Academy of Sciences,Hefei 230031
Abstract：It is convenient for time of flight camera to get the scene depth image,the resolution of depth image is very low due to limitations of the hardware,which can not meet the actual needs. In this paper,a method is proposed for solving depth map super-resolution problem. With a low resolution depth image as input,a high resolution depth map is recovered by using a registered and potentially high resolution camera image of the same scene. The depth map super-resolution problem is solved by developing an optimization framework. Specifically,the reconstruction constraint is applied to get the data term,and based on the fact that discontinuities in range and coloring tend to co-align,laplacian matrix and local structural features of high resolution camera images are used to construct the regularization term. The experimental results demonstrate that the proposed approach gets high resolution range image in terms of both its spatial resolution and depth precision.
杨宇翔，汪增福. 基于彩色图像局部结构特征的深度图超分辨率算法[J]. 模式识别与人工智能, 2013, 26(5): 454-459.
YANG Yu-Xiang,WANG Zeng-Fu. Depth Map Super-Resolution Based on the Local Structural Features of Color Image. , 2013, 26(5): 454-459.
[1]Batlle J,Mouaddib E,Salvi J. Recent Progress in Coded Structured Light as a Technique to Solve the Correspondence Problem: A Survey. Pattern Recognition,1998,31(7): 963-982 [2]2Jarvis R A. A Perspective on Range Finding Techniques for Computer Vision. IEEE Trans on Pattern Analysis and Machine Intelligence,1983,5(2): 122-139 [3]Salvi J,Pagès J,Batlle J. Pattern Codification Strategies in Structured Light Systems. Pattern Recognition,2004,37(4): 827-849 [4]Schuon S,Theobalt C,Davis J,et al. High quality Scanning Using Time of flight Depth Super Resolution // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Anchorage,USA,2008: 1-7 [5]Schuon S,Theobalt C,Davis J,et al. LidarBoost: Depth Super Resolution for ToF 3D Shape Scanning // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Miami,USA,2009: 343-350 [6]Zhang Xudong,Shen Yuliang,Hu Liangmei. Super Resolution Reconstruction Algorithm for PMD Range Image Based on Regularization // Proc of the International Conference on Intelligent Human Machine Systems and Cybernetics. Hangzhou,China,2011,Ⅱ: 15-18 [7]Farsiu S,Robinson M D,Elad M,et al. Fast and Robust Multiframe Super Resolution. IEEE Trans on Image Processing,2004,13(10): 1327-1344 [8]Diebel J,Thrun S. An Application of Markov Random Fields to Range Sensing // Proc of Advances in Neural Information Processing Systems. Vancouver,Canada,2005: 291-298 [9]Lu Jiangbo,Min D,Pahwa R S,et al. A Revisit to MRF Based Depth Map Super Resolution and Enhancement // Proc of the IEEE International Conference on Acoustics,Speech and Signal Processing. Prague,Czech Republic,2011: 985-988 [10]Kopf J,Cohen M F,Lischinski D,et al. Joint Bilateral Upsampling. ACM Trans on Graphics,2007. DOI:10.1145/1275808.1276497 [11]Yang Qingxiong,Yang Ruigang,Davis J,et al. Spatial Depth Super Resolution for Range Images // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis,USA,2007: 1-8 [12]Yang Yuxiang,Wang Zengfu. Range Image Super Resolution via Guided Image Filter // Proc of the 4th International Conference on Internet Multimedia Computing and Service. Wuhan,China,2012: 200-203 [13]He Kaiming,Sun Jian,Tang Xiaoou. Guided Image Filtering // Proc of the 11th European Conference on Computer Vision. Crete,Greece,2010: 1-14 [14]Xiang Xueqin,Li Guangxia,Tong Jing,et al. Fast and Simple Super Resolution for Range Data // Proc of the International Conference on Cyberworlds. Singapore,Singapore,2010: 319-324 [15]Levin A,Lischinshi D,Weiss Y. A Closed Form Solution to Natural Image Matting. IEEE Trans on Pattern Analysis and Machine Intelligence,2008,30(2): 228-242 [16]Chen Jia,Tang Chikeung,Wang Jue. Noise Brush: Interactive High Quality Image Noise Separation. ACM Trans on Graphics,2009,28(5) :1-10
2013, Vol. 26 
