Disparity-constrained stereo endoscopic image super-resolution

With the increasing usage of stereo cameras in computer-assisted surgery techniques, surgeons can benefit from better 3D context of the surgical site in minimally invasive operations. However, since stereo cameras are placed together at the confined endoscope tip, the size of lens and sensors is limited, resulting in low resolution of stereo endoscopic images. How to effectively exploit and utilize stereo information in stereo endoscopic super-resolution (SR) becomes a challenging problem.

In this work, we propose a disparity-constrained stereo super-resolution network (DCSSRnet) to reconstruct images using a stereo image pair. In particular, a disparity constraint mechanism is incorporated into the generation of SR images in the deep neural network framework with effective feature extractors and atrous parallax attention modules.

Extensive experiments were conducted to evaluate the performance of proposed DCSSRnet on the da Vinci dataset and Medtronic dataset. The results on endoscopic image datasets demonstrate that the proposed approach produces a more effective improvement over current SR methods on both quantitative measurements. The ablation studies further verify the effectiveness of the components of the proposed framework.

The proposed DCSSRnet provides a promising solution on enhancing the spatial resolution of stereo endoscopic image pairs. Specifically, the disparity consistency of the stereo image pair provides informative supervision for image reconstruction. The proposed model can serve as a tool for improving the quality of stereo endoscopic images of endoscopic surgery systems.