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.