Détail du document
Identifiant
oai:arXiv.org:2409.14587
Sujet
Computer Science - Computer Vision... Astrophysics - Instrumentation and...Auteur
Hill, Paul Anantrasirichai, Nantheera Achim, Alin Bull, DavidCatégorie
sciences : astrophysique
Année
2024
Date de référencement
25/09/2024
Mots clés
scene approaches deepRésumé
The influence of atmospheric turbulence on acquired imagery makes image interpretation and scene analysis extremely difficult and reduces the effectiveness of conventional approaches for classifying and tracking objects of interest in the scene.
Restoring a scene distorted by atmospheric turbulence is also a challenging problem.
The effect, which is caused by random, spatially varying perturbations, makes conventional model-based approaches difficult and, in most cases, impractical due to complexity and memory requirements.
Deep learning approaches offer faster operation and are capable of implementation on small devices.
This paper reviews the characteristics of atmospheric turbulence and its impact on acquired imagery.
It compares the performance of various state-of-the-art deep neural networks, including Transformers, SWIN and Mamba, when used to mitigate spatio-temporal image distortions.
;Comment: 36 Pages, 8 figures
Hill, Paul,Anantrasirichai, Nantheera,Achim, Alin,Bull, David, 2024, Deep Learning Techniques for Atmospheric Turbulence Removal: A Review
Projection-Specific Heterogeneity of the Axon Initial Segment of Pyramidal Neurons in the Prelimbic Cortex
heterogeneity projection-specific
