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Optimal depth recovery using image guided TGV with depth confidence for high-quality view synthesis.pdf

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Optimal depth recovery using image guided TGV with depth confidence for high-quality view synthesis.pdf

J. Vis. Commun. Image R. 39 (2016) 24–39 Contents lists available at ScienceDirect J. Vis. Commun. Image R. journal homepage: /locate/jvci Optimal depth recovery using image guided TGV with depth con?dence for high-quality view synthesis Pongsak Lasang a,b,?, Wuttipong Kumwilaisak c, Yazhou Liu d, Sheng Mei Shen a a Core Technology Group, Panasonic RD Center Singapore, Singapore b Department of Computer Engineering, King Mongkut′s University of Technology Thonburi, Bangkok, Thailand c Department of Electronics and Telecommunication Engineering, King Mongkut′s University of Technology Thonburi, Bangkok, Thailand d Department of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210048, China article info Article history: Received 27 May 2015 Revised 13 April 2016 Accepted 10 May 2016 Available online 12 May 2016 Keywords: RGB-D sensors Depth con?dence Depth recovery Depth Image Based Rendering (DIBR) View synthesis Hole ?lling 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 con?dence. A new directional hole ?lling 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. ó 2016 Elsevier Inc. All rights reserved. 1. Introduction A RGB-D sensor is one of the fundamental sensing devices and plays an essential role in many vision processing applications. It can capture both visual texture and geometrical structure information of a scene. With the rapid development of sensor technologies and a great invention of Kinect’s sensor [1], a real-time