Computer Vision and Pattern Recognition (CVPR 2018) |
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| Enhancing the Spatial Resolution of Stereo Images using a Parallax Prior |
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Daniel S. Jeon |
Seung-Hwan Baek |
Inchang Choi |
Min H. Kim |
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Korea Advanced Institute of Science and Technology (KAIST) |
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Compared to a naive approach of bicubic upsampling, Bhavsar et al. enhance the spatial resolution but suffer from jaggy aliasing artifacts. The proposed method can enhance the spatial resolution significantly by taking advantage of stereo input. |
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Abstract |
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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.
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@InProceedings{Jeonetal:CVPR:2018,
author = {Daniel S. Jeon and Seung-Hwan Baek and Inchang Choi
and Min H. Kim},
title = {Enhancing the Spatial Resolution of Stereo Images
using a Parallax Prior},
booktitle = {Proc. IEEE Computer Vision and Pattern Recognition
(CVPR 2018)},
publisher = {IEEE},
address = {Salt Lake City, Utah, United States},
year = {2018},
pages = {},
volume = {},
}
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Hosted by Visual Computing Laboratory, School of Computing, KAIST.
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