Stereo Endoscopic Image Super-Resolution Using Disparity-Constrained Parallel Attention
This work addresses the problem of enhancing spatial resolution in stereo image pairs for endoscopic surgery, providing a domain-specific incremental improvement.
The paper tackles the challenge of super-resolving stereo endoscopic images by proposing a disparity-constrained network (DCSSRnet) that incorporates disparity-based constraints and atrous parallax-attention modules, resulting in outperformance over current methods in quantitative and qualitative evaluations on laparoscopic images.
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.