@inproceedings{196b472e8fbe4bc4a5acd4addf360278,
title = "A Convolutional Recurrent Mixer Network For Radar Meteorological Image Super-Resolution",
abstract = "Image super-resolution (SR) focuses on reconstructing high-resolution images from their low-resolution counterparts, often affected by sensor limitations or environmental factors. Convolutional Neural Networks (CNNs) are state-of-the-art for SR tasks but computationally heavy. This paper introduces a novel CRMN (Convolutional Recurrent Mixer Network), a hybrid deep learning-based SR technique designed to address the complexity of CNNs, which is validated in the context of meteorological radar images. Experiments on public benchmark datasets (Berkley432 and T291) and our newly manually collected precipitation dataset from the Meteorological Research Institute (IPMET) show that our CRMN model provides competitive results compared to leading SR methods with significantly fewer parameters, making it a promising and practical solution for SR applications, particularly radar meteorology.",
keywords = "Convolutional Neural Networks, Deep Learning, Meteorology, Recurrent Mixer Network, Super Resolution",
author = "Pires, \{Rafael Gon{\c c}alves\} and Santos, \{Daniel F.S.\} and Calheiros, \{Roberto V.\} and Papa, \{Jo{\~a}o Paulo\} and Lee, \{Ik Hyun\} and Sambit Bakshi and Khan Muhammad",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025 ; Conference date: 06-04-2025 Through 11-04-2025",
year = "2025",
doi = "10.1109/ICASSP49660.2025.10887893",
language = "English",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Rao, \{Bhaskar D\} and Isabel Trancoso and Gaurav Sharma and Mehta, \{Neelesh B.\}",
booktitle = "2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025 - Proceedings",
}