  With the popularity of stereo cameras in computer assisted surgery
techniques, a second viewpoint would provide additional information in surgery.
However, how to effectively access and use stereo information for the
super-resolution (SR) purpose is often a challenge. In this paper, we propose a
disparity-constrained stereo super-resolution network (DCSSRnet) to
simultaneously compute a super-resolved image in a stereo image pair. In
particular, we incorporate a disparity-based constraint mechanism into the
generation of SR images in a deep neural network framework with an additional
atrous parallax-attention modules. Experiment results on laparoscopic images
demonstrate that the proposed framework outperforms current SR methods on both
quantitative and qualitative evaluations. Our DCSSRnet provides a promising
solution on enhancing spatial resolution of stereo image pairs, which will be
extremely beneficial for the endoscopic surgery.
