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Proceedings Paper

Depth image enhancement using perceptual texture priors
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Paper Abstract

A depth camera is widely used in various applications because it provides a depth image of the scene in real time. However, due to the limited power consumption, the depth camera presents severe noises, incapable of providing the high quality 3D data. Although the smoothness prior is often employed to subside the depth noise, it discards the geometric details so to degrade the distance resolution and hinder achieving the realism in 3D contents.

In this paper, we propose a perceptual-based depth image enhancement technique that automatically recovers the depth details of various textures, using a statistical framework inspired by human mechanism of perceiving surface details by texture priors. We construct the database composed of the high quality normals. Based on the recent studies in human visual perception (HVP), we select the pattern density as a primary feature to classify textures. Upon the classification results, we match and substitute the noisy input normals with high quality normals in the database. As a result, our method provides the high quality depth image preserving the surface details. We expect that our work is effective to enhance the details of depth image from 3D sensors and to provide a high-fidelity virtual reality experience.

Paper Details

Date Published: 17 March 2015
PDF: 8 pages
Proc. SPIE 9394, Human Vision and Electronic Imaging XX, 93941C (17 March 2015); doi: 10.1117/12.2083094
Show Author Affiliations
Duhyeon Bang, Yonsei Univ. (Korea, Republic of)
Hyunjung Shim, Yonsei Univ. (Korea, Republic of)


Published in SPIE Proceedings Vol. 9394:
Human Vision and Electronic Imaging XX
Bernice E. Rogowitz; Thrasyvoulos N. Pappas; Huib de Ridder, Editor(s)

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