Proceedings ArticleDOI
Enhancing the Spatial Resolution of Stereo Images Using a Parallax Prior
Daniel S. Jeon,Seung-Hwan Baek,Inchang Choi,Min H. Kim +3 more
- pp 1721-1730
TLDR
A novel method to learn a parallax prior from stereo image datasets by jointly training two-stage networks that enhances the spatial resolution of stereo images significantly more than single-image super-resolution methods.Abstract:
We present a novel method that can enhance the spatial resolution of stereo images using a parallax prior. While traditional stereo imaging has focused on estimating depth from stereo images, our method utilizes stereo images to enhance spatial resolution instead of estimating disparity. The critical challenge for enhancing spatial resolution from stereo images: how to register corresponding pixels with subpixel accuracy. Since disparity in traditional stereo imaging is calculated per pixel, it is directly inappropriate for enhancing spatial resolution. We, therefore, learn a parallax prior from stereo image datasets by jointly training two-stage networks. The first network learns how to enhance the spatial resolution of stereo images in luminance, and the second network learns how to reconstruct a high-resolution color image from high-resolution luminance and chrominance of the input image. Our two-stage joint network enhances the spatial resolution of stereo images significantly more than single-image super-resolution methods. The proposed method is directly applicable to any stereo depth imaging methods, enabling us to enhance the spatial resolution of stereo images.read more
Citations
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Journal ArticleDOI
Deep Learning for Image Super-Resolution: A Survey
TL;DR: A survey on recent advances of image super-resolution techniques using deep learning approaches in a systematic way, which can roughly group the existing studies of SR techniques into three major categories: supervised SR, unsupervised SR, and domain-specific SR.
Proceedings ArticleDOI
Learning Parallax Attention for Stereo Image Super-Resolution
TL;DR: A parallax-attention mechanism with a global receptive field along the epipolar line to handle different stereo images with large disparity variations is introduced and a new and the largest dataset for stereo image SR is proposed.
Proceedings ArticleDOI
Flickr1024: A Large-Scale Dataset for Stereo Image Super-Resolution
TL;DR: Experimental results show that, as compared to the KITTI and Middlebury datasets, the Flickr1024 dataset can help to handle the over-fitting problem and significantly improves the performance of stereo SR methods.
Journal ArticleDOI
A Stereo Attention Module for Stereo Image Super-Resolution
TL;DR: Using SAM, the generic stereo attention module (SAM) is proposed to extend arbitrary SISR networks to stereo images and can exploit cross-view information while maintaining the superiority of intra-View information exploitation, resulting in notable performance gain to SisR networks.
Proceedings ArticleDOI
DAVANet: Stereo Deblurring With View Aggregation
TL;DR: In this article, the authors proposed a stereo image deblurring network with depth awareness and view aggregation, named DAVANet, to remove complex spatially-varying blur in dynamic scenes.
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