Back to articles
Articles
Volume: 32 | Article ID: art00015
Image
ProPaCoL-Net: A Novel Recursive Stereo Image SR Network with Progressive Parallax Coherency Learning
  DOI :  10.2352/ISSN.2470-1173.2020.14.COIMG-342  Published OnlineJanuary 2020
Abstract

Recently, stereo cameras have been widely packed in smart phones and autonomous vehicles thanks to low cost and smallsized packages. Nevertheless, acquiring high resolution (HR) stereo images is still a challenging problem. While the traditional stereo image processing tasks have mainly focused on stereo matching, stereo super-resolution (SR) has drawn less attention which is necessitated for HR images. Some deep learning based stereo image SR works have recently shown promising results. However, they have not fully exploited binocular parallax in SR, which may lead to unrealistic visual perception. In this paper, we present a novel and computationally efficient convolutional neural network (CNN) based deep SR network for stereo images by learning parallax coherency between the left and right SR images, which is called ProPaCoL-Net. The proposed ProPaCoL-Net progressively learns parallax coherency via a novel recursive parallax coherency (RPC) module with shared parameters. The RPC module is effectively designed to extract parallax information in prior for the left image SR from its right view input images and vice versa. Furthermore, we propose a parallax coherency loss to reliably train the ProPaCoL-Net. From extensive experiments, the ProPaCoL-Net shows to outperform the very recent state-of-the-art method with average 1.15 dB higher in PSNR.

Subject Areas :
Views 18
Downloads 7
 articleview.views 18
 articleview.downloads 7
  Cite this article 

Jeonghun Kim, Munchurl Kim, "ProPaCoL-Net: A Novel Recursive Stereo Image SR Network with Progressive Parallax Coherency Learningin Proc. IS&T Int’l. Symp. on Electronic Imaging: Computational Imaging XVIII,  2020,  pp 342-1 - 342-8,  https://doi.org/10.2352/ISSN.2470-1173.2020.14.COIMG-342

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2020
72010604
Electronic Imaging
2470-1173
Society for Imaging Science and Technology
7003 Kilworth Lane, Springfield, VA 22151 USA