Abstract
With the popularity of dual cameras in recently released smart phones, a growing number of super-resolution (SR) methods have been proposed to enhance the resolution of stereo image pairs. However, the lack of high-quality stereo datasets has limited the research in this area. To facilitate the training and evaluation of novel stereo SR algorithms, in this paper, we propose a large-scale stereo dataset named Flickr1024. Compared to the existing stereo datasets, the proposed dataset contains much more high-quality images and covers diverse scenarios. We train two state-of-the-art stereo SR methods (i.e., StereoSR and PASSRnet) on the KITTI2015, Middlebury, and Flickr1024 datasets. Experimental results demonstrate that our dataset can improve the performance of stereo SR algorithms. The Flickr1024 dataset is available online at: https://yingqianwang.github.io/Flickr1024.
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URL
http://arxiv.org/abs/1903.06332