Next Article in Journal
A Rapid Convergent Low Complexity Interference Alignment Algorithm for Wireless Sensor Networks
Previous Article in Journal
V-Alert: Description and Validation of a Vulnerable Road User Alert System in the Framework of a Smart City
Article Menu

Export Article

Open AccessArticle
Sensors 2015, 15(8), 18506-18525; doi:10.3390/s150818506

Temporal and Spatial Denoising of Depth Maps

1
Department of Computer Science and Information Engineering, National Taipei University, New Taipei City 23741, Taiwan
2
Department of Biomedical Engineering, Yuanpei University of Medical Technology, Hsinchu 30015, Taiwan
3
Department of Software Engineering and Management, National Kaohsiung Normal University, Kaohsiung 82444, Taiwan
4
Graduate Institute of Electronics Engineering, National Taiwan University, Taipei 10617, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M.N. Passaro
Received: 3 May 2015 / Revised: 22 July 2015 / Accepted: 23 July 2015 / Published: 29 July 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [2168 KB, uploaded 29 July 2015]   |  

Abstract

This work presents a procedure for refining depth maps acquired using RGB-D (depth) cameras. With numerous new structured-light RGB-D cameras, acquiring high-resolution depth maps has become easy. However, there are problems such as undesired occlusion, inaccurate depth values, and temporal variation of pixel values when using these cameras. In this paper, a proposed method based on an exemplar-based inpainting method is proposed to remove artefacts in depth maps obtained using RGB-D cameras. Exemplar-based inpainting has been used to repair an object-removed image. The concept underlying this inpainting method is similar to that underlying the procedure for padding the occlusions in the depth data obtained using RGB-D cameras. Therefore, our proposed method enhances and modifies the inpainting method for application in and the refinement of RGB-D depth data image quality. For evaluating the experimental results of the proposed method, our proposed method was tested on the Tsukuba Stereo Dataset, which contains a 3D video with the ground truths of depth maps, occlusion maps, RGB images, the peak signal-to-noise ratio, and the computational time as the evaluation metrics. Moreover, a set of self-recorded RGB-D depth maps and their refined versions are presented to show the effectiveness of the proposed method. View Full-Text
Keywords: depth image; spatial-temporal denoising; RGB-D sensor; hole padding depth image; spatial-temporal denoising; RGB-D sensor; hole padding
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Lin, B.-S.; Su, M.-J.; Cheng, P.-H.; Tseng, P.-J.; Chen, S.-J. Temporal and Spatial Denoising of Depth Maps. Sensors 2015, 15, 18506-18525.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top