Explicit modeling on depth-color inconsistency for color-guided depth up-sampling

Color-guided depth up-sampling is to enhance the resolution of depth map according to the assumption that the depth discontinuity and color image edge at the corresponding location are consistent. Through all methods reported, MRF including its variants is one of major approaches, which has dominated in this area for several years. However, the assumption above is not always true. Solution usually is to adjust the weighting inside smoothness term in MRF model. But there is no any method explicitly considering the inconsistency occurring between depth discontinuity and the corresponding color edge. In this paper, we propose quantitative measurement on such inconsistency and explicitly embed it into weighting value of smoothness term. Such solution has not been reported in the literature. The improved depth up-sampling based on the proposed method is evaluated on Middlebury datasets and ToFMark datasets and demonstrate promising results.

[1]  J. M. Hammersley,et al.  Markov fields on finite graphs and lattices , 1971 .

[2]  Horst Bischof,et al.  Image Guided Depth Upsampling Using Anisotropic Total Generalized Variation , 2013, 2013 IEEE International Conference on Computer Vision.

[3]  Chang-Su Kim,et al.  SEQM: Edge quality assessment based on structural pixel matching , 2012, 2012 Visual Communications and Image Processing.

[4]  Raanan Fattal,et al.  Image and video upscaling from local self-examples , 2011, TOGS.

[5]  Ruigang Yang,et al.  Spatial-Depth Super Resolution for Range Images , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Jian Sun,et al.  Guided Image Filtering , 2010, ECCV.

[7]  Minh N. Do,et al.  A revisit to MRF-based depth map super-resolution and enhancement , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[8]  H. Kuhn The Hungarian method for the assignment problem , 1955 .

[9]  Lifeng Sun,et al.  Joint Example-Based Depth Map Super-Resolution , 2012, 2012 IEEE International Conference on Multimedia and Expo.

[10]  Martin Kleinsteuber,et al.  A Joint Intensity and Depth Co-sparse Analysis Model for Depth Map Super-resolution , 2013, 2013 IEEE International Conference on Computer Vision.

[11]  Minh N. Do,et al.  Depth Video Enhancement Based on Weighted Mode Filtering , 2012, IEEE Transactions on Image Processing.

[12]  Ruigang Yang,et al.  Spatial-Temporal Fusion for High Accuracy Depth Maps Using Dynamic MRFs , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Michael S. Brown,et al.  High quality depth map upsampling for 3D-TOF cameras , 2011, 2011 International Conference on Computer Vision.

[14]  Sebastian Thrun,et al.  An Application of Markov Random Fields to Range Sensing , 2005, NIPS.

[15]  Ming-Yu Liu,et al.  Joint Geodesic Upsampling of Depth Images , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Reinhard Koch,et al.  Time‐of‐Flight Cameras in Computer Graphics , 2010, Comput. Graph. Forum.

[17]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Guillermo Sapiro,et al.  Sparse Representations for Range Data Restoration , 2012, IEEE Transactions on Image Processing.

[19]  M. Hestenes,et al.  Methods of conjugate gradients for solving linear systems , 1952 .

[20]  Sebastian Thrun,et al.  LidarBoost: Depth superresolution for ToF 3D shape scanning , 2009, CVPR.

[21]  Johannes R. Sveinsson,et al.  TOF-CCD image fusion using complex wavelets , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[22]  Olga Veksler,et al.  Fast Approximate Energy Minimization via Graph Cuts , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Dani Lischinski,et al.  Joint bilateral upsampling , 2007, SIGGRAPH 2007.