A new convolutional neural network is proposed for hole filling in the synthesized virtual view generated by depth image-based rendering (DIBR). A context encoder in the network is trained to make predictions of the hole region based on the rendered virtual view, with an adversarial discriminator reducing the errors and producing sharper and more precise result. A texture network in the end of the framework extracts the style of the image and achieves a natural output which is closer to reality. The experiment results demonstrate both subjectively and objectively that the proposed method obtain better 3D video quality compared to previous methods. The average peak signal-to-noise ratio (PSNR) increases by 0.36 dB.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.