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.

翻译：SEGSRNet解决了低分辨率立体内窥镜图像中手术器械精确识别这一医学成像与机器人手术中的常见难题。该创新框架通过在分割前应用最先进的超分辨率技术，增强图像清晰度与分割精度，确保为更精确的分割提供更高质量的输入。SEGSRNet融合了高级特征提取、注意力机制与空间处理技术，以锐化图像细节，这对于医学图像中器械的准确识别具有重要意义。所提出的模型在Dice系数、交并比（IoU）、峰值信噪比（PSNR）及结构相似性（SSIM）指标上均优于现有模型，可为立体内窥镜手术成像生成更清晰、更精确的图像。SEGSRNet能够提供高分辨率图像与精准分割，从而显著提升手术精度与患者护理效果。