An Improved Image Super-Resolution Algorithm for Percutaneous Endoscopic Lumbar Discectomy

High-resolution (HR) spinal endoscopic images are essential to enhance the surgeon’s visual presence for the guidance of surgical procedures. However, available image super-resolution methods, especially deep learning methods, are mostly trained with open-source life scene datasets which possess limited medical image features. To address this issue, we have proposed an improved SRGAN model for the visual enhancement of percutaneous endoscopic lumbar discectomy (PELD) surgical images. Specifically, a residual dense block (RDB) and a dynamic RELU function are introduced. We validate the proposed method on PELD datasets. Quantitative and qualitative comparisons are carried out by comparing methods. The method proposed in this paper improves PSNR by 2.8% and SSIM by 6% compared with the original SRGAN, which proves the superiority of this methods.