Depth map enhancement using adaptive steering Kernel regression based on distance transform

Authors:
Sung-Yeol Kim;Woon Cho;Andreas Koschan;Mongi A. Abidi
Affiliations:
Imaging, Robotics and Intelligent System Lab, The University of Tennessee, Knoxville, TN;Imaging, Robotics and Intelligent System Lab, The University of Tennessee, Knoxville, TN;Imaging, Robotics and Intelligent System Lab, The University of Tennessee, Knoxville, TN;Imaging, Robotics and Intelligent System Lab, The University of Tennessee, Knoxville, TN
Venue:
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
Year:
2011

Citing 8
Cited 0

Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms

International Journal of Computer Vision
Digital photography with flash and no-flash image pairs

ACM SIGGRAPH 2004 Papers
Generation of high-quality depth maps using hybrid camera system for 3-D video

Journal of Visual Communication and Image Representation
Real-time human pose recognition in parts from single depth images

CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Three-dimensional natural video system based on layered representation of depth maps

IEEE Transactions on Consumer Electronics
On the origin of the bilateral filter and ways to improve it

IEEE Transactions on Image Processing
Deblurring Using Regularized Locally Adaptive Kernel Regression

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we present a method to enhance noisy depth maps using adaptive steering kernel regression based on distance transform. Data-adaptive kernel regression filters are widely used for image denoising by considering spatial and photometric properties of pixel data. In order to reduce noise in depth maps more efficiently, we adaptively refine the steering kernel regression function according to local region structures, flat and textured areas. In this work, we first generate two distance transform maps from the depth map and its corresponding color image. Then, the steering kernel is modified by a newly-designed weighing function directly related to joint distance transform. The weighting function expands the steering kernel in flat areas and shrinks it in textured areas toward local edges in the depth map. Finally, we filter the noise in the depth map with the refined steering kernel regression function. Experimental results show that our method outperforms the competing methods in objective and subjective comparisons for depth map enhancement.