  In Robot-Assisted Minimally Invasive Surgery (RAMIS), a camera assistant is
normally required to control the position and zooming ratio of the laparoscope,
following the surgeon's instructions. However, moving the laparoscope
frequently may lead to unstable and suboptimal views, while the adjustment of
zooming ratio may interrupt the workflow of the surgical operation. To this
end, we propose a multi-scale Generative Adversarial Network (GAN)-based video
super-resolution method to construct a framework for automatic zooming ratio
adjustment. It can provide automatic real-time zooming for high-quality
visualization of the Region Of Interest (ROI) during the surgical operation. In
the pipeline of the framework, the Kernel Correlation Filter (KCF) tracker is
used for tracking the tips of the surgical tools, while the Semi-Global Block
Matching (SGBM) based depth estimation and Recurrent Neural Network (RNN)-based
context-awareness are developed to determine the upscaling ratio for zooming.
The framework is validated with the JIGSAW dataset and Hamlyn Centre
Laparoscopic/Endoscopic Video Datasets, with results demonstrating its
practicability.
