E-SEVSR- Edge Guided Stereo Endoscopic Video Super-Resolution

Integrating Stereo Imaging technology into medical diagnostics and surgeries marks a significant revolution in medical sciences. This advancement gives surgeons and physicians a deeper understanding of patients’ organ anatomy. However, like any technology, stereo cameras have their limitations, such as low resolution (LR) and output images that are often blurry. Our paper introduces a novel approach—a multi-stage network with a pioneering Stereo Endoscopic Attention Module (SEAM). This network aims to progressively enhance the quality of super-resolution (SR), moving from coarse to fine details. Specifically, we propose an edge-guided stereo attention mechanism integrated into each interaction of stereo features. This mechanism aims to capture consistent structural details across different views more effectively. Our proposed model demonstrates superior super-resolution reconstruction performance through comprehensive quantitative evaluations and experiments conducted on three datasets. Our E-SEVSR framework demonstrates superiority over alternative approaches. This framework leverages the edge-guided stereo attention mechanism within the multi-stage network, improving super-resolution quality in medical imaging applications.