SRCNet: Super-resolution Networks for Capsule Endoscope Robots
Menglu Tan, Guangdong Zhan, Zijin Zeng, Ao Wang, Lin Feng
Abstract
In recent years, capsule robots have gained wide acceptance among doctors and patients for the examination of gastrointestinal diseases due to their non-invasive, safe, and painless advantages. However, the image resolution captured by capsule robots is limited by space size and power, which hinders doctors' ability to accurately assess patients' stomach conditions and real-time control of the capsule robot. This paper proposes the design of two super-resolution networks for capsule robot videos. The first network, EndoVSR, is a high-performance offline video super-resolution network based on a generative adversarial network. It is designed to enhance the resolution of captured videos during offline processing. The second network, Bi-RUN, is a real-time video super-resolution network based on recurrent neural networks. It is designed to enhance the resolution of videos in real-time, enabling doctors to have a clearer view of the stomach condition during the examination. Extensive training and verification of these networks have been conducted using different datasets. All the performance indicators achieved leading positions. Furthermore, simulation experiments were carried out on pig stomachs in vitro to further validate the performance of the proposed networks in practical applications.
BibTeX
@inproceedings{iros2025_srcnetsuperresol,
title = {SRCNet: Super-resolution Networks for Capsule Endoscope Robots},
author = {Menglu Tan and Guangdong Zhan and Zijin Zeng and Ao Wang and Lin Feng},
booktitle = {IROS 2025},
year = {2025}
}