Using MIRNet for Low Light Image Enhancement

Ethan Chen, Robail Yasrab, Pramit Saha

2025

Abstract

This study explores the application of MIRNet (Multi-scale Image Restoration Network), a deep learning architecture designed for image enhancement. MIRNet uses convolutional neural networks (CNNs) to capture image details and textures at various scales, enabling effective restoration and enhancement of low-quality images. Experiments were conducted using the LoL and SICE datasets to validate and optimize MIRNet’s performance. The results were compared with an existing image enhancement tool, demonstrating the superior effectiveness of MIRNet even with architectural modifications or training on different data sets. The research also explains MIRNet’s architecture and its approach to processing and enhancing image content. This work highlights MIRNet’s potential to advance image enhancement through deep learning techniques.

Paper Citation

in Harvard Style

Chen E., Yasrab R. and Saha P. (2025). Using MIRNet for Low Light Image Enhancement. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING; ISBN 978-989-758-731-3, SciTePress, pages 280-287. DOI: 10.5220/0013099600003911

in Bibtex Style

@conference{bioimaging25,
author={Ethan Chen and Robail Yasrab and Pramit Saha},
title={Using MIRNet for Low Light Image Enhancement},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING},
year={2025},
pages={280-287},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013099600003911},
isbn={978-989-758-731-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING
TI - Using MIRNet for Low Light Image Enhancement
SN - 978-989-758-731-3
AU - Chen E.
AU - Yasrab R.
AU - Saha P.
PY - 2025
SP - 280
EP - 287
DO - 10.5220/0013099600003911
PB - SciTePress