Stereo attention-based all-in-one super-resolution for robot-assisted minimally invasive surgery

Robot-assisted minimally invasive surgery (MIS) faces challenges in obtaining high-quality imaging results due to the limited spatial environment. In this paper, we present an all-in-one image super-resolution (SR) algorithm designed to tackle this challenge. By utilizing the stereo information from binocular images, we effectively convert low-resolution images into high-resolution ones. Our model architecture amalgamates the prowess of Convolutional Neural Networks (CNNs) and Transformers, capitalizing on the advantages of both methodologies. To achieve super-resolution across all scale factors, we employ a trainable upsampling module within our proposed network. We substantiate the effectiveness of our method through extensive quantitative and qualitative experiments. The results of our evaluations provide strong evidence supporting the superior performance of our approach in enhancing the quality of surgical images. Our method improves the resolution and thus the overall image quality, which allows the surgeon to perform precise operations conveniently. Simultaneously, it also facilitates the scaling of the region of interest (ROI) to achieve high-quality visualization during surgical procedures. Furthermore, it has the potential to enhance the image quality during telesurgery.