Optimal depth recovery using image guided TGV with depth confidence for high-quality view synthesis
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Author list: Lasang P., Kumwilaisak W., Liu Y., Shen S.M.
Publisher: Elsevier
Publication year: 2016
Journal: Journal of Visual Communication and Image Representation (1047-3203)
Volume number: 39
Start page: 24
End page: 39
Number of pages: 16
ISSN: 1047-3203
eISSN: 1095-9076
Languages: English-Great Britain (EN-GB)
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Abstract
This paper presents a new depth image recovery method for RGB-D sensors giving a complete, sharp, and accurate object shape from a noisy boundary depth map. The proposed method uses the image guided Total Generalized Variation (TGV) with the depth confidence. A new directional hole filling method of view synthesis is also investigated to produce natural texture in hole regions whereas reducing blurring effect and preventing distortion. Thus, a high-quality image view can be achieved. Experimental results show that the proposed method yields higher quality recovered depth maps and synthesized image views than other previous methods. ฉ 2016 Elsevier Inc. All rights reserved.
Keywords
Depth confidence, Depth Image Based Rendering (DIBR), Hole filling, RGB-D sensors, View synthesis
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