Super-Resolution is the image processing technique to restore high-resolution images from low-resolution images.

In this work, a fast and low-latency super-resolution system has been implemented using a field-programmable gate array (FPGA), a device on which users can implement arbitrary logic circuits.

The system is based on a convolutional neural network (CNN). Unlike common CNN processors, the whole network is mapped on an FPGA as a pipeline, making the best use of parallelism for better performance.

Laparoscopy is an operation performed through small incisions using a camera system called a laparoscope. If a camera assistant, who maneuvers the laparoscope, can be replaced with a robot, it would be beneficial in the area suffering from lack of medical workers.

The objective of this work is to realize the CNN-based organ segmentation system for cholecystectomy used for automated laparoscope control.

Considering a small dataset resulting from the highly specialized task, regularization techniques and a devised network structure are used to prevent overfitting.