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Open Access research that better understands changing marine ecologies...

Strathprints makes available scholarly Open Access content by researchers in the Department of Mathematics & Statistics.

Mathematics & Statistics hosts the Marine Population Modelling group which is engaged in research into topics surrounding marine resource modelling and ecology. Recent work has included important developments in the population modelling of marine species.

Explore the Open Access research of Mathematics & Statistics. Or explore all of Strathclyde's Open Access research...

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Group by: Publication Date | Item type | No Grouping
Jump to: 2024 | 2023 | 2021 | 2020 | 2019
Number of items: 5.

2024

Vint, David and Di Caterina, Gaetano and Kirkland, Paul and Lamb, Robert (2024) Deep learning-based turbulence mitigation for long-range imaging. Proceedings of SPIE: The International Society for Optical Engineering, 13206. 132060Z. ISSN 0277-786X

2023

Vint, D. and Di Caterina, G. and Kirkland, P. and Lamb, R.A. and Humphreys, D. (2023) Simulation of anisoplanatic turbulence for images and videos. In: Sensor Signal Processing for Defence 2023, 2023-09-12 - 2023-09-13.

2021

Vint, David and Anderson, Matthew and Yang, Yuhao and Ilioudis, Christos and Di Caterina, Gaetano and Clemente, Carmine (2021) Automatic target recognition in low resolution foliage penetrating SAR using CNNs and GANs. Remote Sensing, 13 (4). 596. ISSN 2072-4292

2020

Vint, David and Di Caterina, Gaetano and Soraghan, John and Lamb, Robert and Humphreys, David; Dijk, Judith, ed. (2020) Analysis of deep learning architectures for turbulence mitigation in long-range imagery. In: Artificial Intelligence and Machine Learning in Defense Applications II. SPIE, Bellingham, WA..

2019

Vint, D. and Di Caterina, G. and Soraghan, J. J. and Lamb, R. A. and Humphreys, D. (2019) Evaluation of performance of VDSR super resolution on real and synthetic images. In: Sensor Signal Processing for Defence 2019, 2019-05-09 - 2019-05-10.

This list was generated on Sat Dec 21 15:01:27 2024 GMT.