A Convolutional Recurrent Mixer Network For Radar Meteorological Image Super-Resolution - Pires, Rafael Gonçalves [UNESP]; Santos, Daniel F. S [UNESP]; Calheiros, Roberto V. [UNESP]; Papa, João Paulo [UNESP]; Lee, Ik Hyun; Bakshi, Sambit; Muhammad, Khan (Institute of Electrical and Electronics Engineers (IEEE), 2025)
Image super-resolution (SR) focuses on reconstructing high-resolution images from their low-resolution counter-parts, 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.