  SEGSRNet addresses the challenge of precisely identifying surgical
instruments in low-resolution stereo endoscopic images, a common issue in
medical imaging and robotic surgery. Our innovative framework enhances image
clarity and segmentation accuracy by applying state-of-the-art super-resolution
techniques before segmentation. This ensures higher-quality inputs for more
precise segmentation. SEGSRNet combines advanced feature extraction and
attention mechanisms with spatial processing to sharpen image details, which is
significant for accurate tool identification in medical images. Our proposed
model outperforms current models including Dice, IoU, PSNR, and SSIM, SEGSRNet
where it produces clearer and more accurate images for stereo endoscopic
surgical imaging. SEGSRNet can provide image resolution and precise
segmentation which can significantly enhance surgical accuracy and patient care
outcomes.
