A Hole-filling Method for DIBR Based on Convolutional Neural Network

Yongrui Li; Xinzhu Sang; Duo Chen; Peng Wang; Huachun Wang; Jinhui Yuan; Jinhui Yuan; Kuiru Wang; Binbin Yan

doi:10.1364/CLEOPR.2018.F1F.5

CLEO Pacific Rim Conference 2018
OSA Technical Digest (Optica Publishing Group, 2018),
paper F1F.5
•https://doi.org/10.1364/CLEOPR.2018.F1F.5

A Hole-filling Method for DIBR Based on Convolutional Neural Network

Yongrui Li, Xinzhu Sang, Duo Chen, Peng Wang, Huachun Wang, Jinhui Yuan, Kuiru Wang, and Binbin Yan

Conference on Lasers and Electro-Optics/Pacific Rim 2018

Hong Kong China
29 July–3 August 2018
ISBN: 978-1-943580-45-3

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Related Technologies and Applications for Imaging, Display and Storage (F1F)

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Y. Li, X. Sang, D. Chen, P. Wang, H. Wang, J. Yuan, K. Wang, and B. Yan, "A Hole-filling Method for DIBR Based on Convolutional Neural Network," in CLEO Pacific Rim Conference 2018, OSA Technical Digest (Optica Publishing Group, 2018), paper F1F.5.

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Abstract

There are holes for images generated by Depth-Image-Based Rendering (DIBR) method due to occlusion of foreground. An algorithm to fill holes based on convolutional neural networks is presented. PSNR of the image after filling holes is 32.65dB.

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