2024/09/03 by Paul Hill, Nantheera Anantrasirichai, Hill, Paul +5 · 4 citations
Computer Science · Earth and Planetary Sciences · Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Physical sciences #Image and Signal Denoising Methods #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Meteorological Phenomena and Simulations #Traffic Prediction and Management Techniques
paper · pdf · doi:10.48550/arxiv.2409.14587
openalex publication_date 2024/09/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.