NDLI: Learning across views for stereo image completion

Content Provider	IET Digital Library
Author	Ma, Wei Zheng, Mana Ma, Wenguang Xu, Shibiao Zhang, Xiaopeng
Abstract	Stereo image completion (SIC) is to fill holes existing in a pair of stereo images. SIC is more complicated than single image repairing, which needs to complete the pair of images while keeping their stereoscopic consistency. In recent years, deep learning has been introduced into single image repairing but seldom used for SIC. The authors present a novel deep learning-based approach for SIC. In their method, an X-shaped fully convolutional network (called SICNet) is proposed and designed to complete stereo images, which is composed of two branches of convolutional neural network layers to encode the context of the left and right images separately, a fusion module for stereo-interactive completion, and two branches of decoders to produce completed left and right images, respectively. In consideration of both inter-view and intra-view cues, they introduce auxiliary networks and define comprehensive losses to train SICNet to perform single-view coherent and cross-view consistent completion simultaneously. Extensive experiments are conducted to show the state-of-the-art performances of the proposed approach and its key components.
Starting Page	482
Ending Page	492
Page Count	11
ISSN	17519632
Volume Number	14
e-ISSN	17519640
Issue Number	Issue 7, Oct (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/14/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2019.0775
Journal	IET Computer Vision
Publisher Date	2020-05-12
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique Convolutional Neural Nets Convolutional Neural Network Layer Cross-view Consistent Completion Deep Learning Fusion Module Image Fusion Image Segmentation Learning in AI Neural Computing Technique Optical, Image And Video Signal Processing Sensor Fusion SICNet Single Image Repairing Single-view Coherent Stereo Image Completion Stereo Image Processing Stereo-interactive Completion X-shaped Fully Convolutional Network
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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