Document Type
Article
Publication Date
11-19-2025
Department
College of Computing
Abstract
In recent years, Single-Image Super-Resolution (SISR) has gained significant attention in the geoscience and remote sensing community for its potential to improve the resolution of low-quality underwater imagery. This paper introduces MIMAR-Net (Multiscale Inception-based Manhattan Attention Residual Network), a new deep learning architecture designed to increase the spatial resolution of input color images. MIMAR-Net integrates a multiscale inception module, cascaded residue learning, and advanced attention mechanisms, such as the MaSA layer, to capture both local and global contextual information effectively. By utilizing multiscale processing and advanced attention strategies, MIMAR-Net allows us to handle the complexities of underwater environments with precision and robustness. We evaluate the model on three popular underwater image datasets, namely UFO-120, USR-248, and EUVP, and perform extensive comparisons against state-of-the-art methods. Experimental results demonstrate that MIMAR-Net consistently outperforms existing approaches, achieving superior qualitative and quantitative improvements in image quality, making it a reliable solution for underwater image enhancement in various challenging scenarios.
Publication Title
Electronics Switzerland
Recommended Citation
Zahan, N.,
Paheding, S.,
Saleem, A.,
Havens, T. C.,
&
Esselman, P.
(2025).
MIMAR-Net: Multiscale Inception-Based Manhattan Attention Residual Network and Its Application to Underwater Image Super-Resolution.
Electronics Switzerland,
14(22).
http://doi.org/10.3390/electronics14224544
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p2/2222
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Version
Publisher's PDF
Publisher's Statement
Copyright: © 2025 by the authors. Publisher’s version of record: https://doi.org/10.3390/electronics14224544