Evaluation of neural network algorithms for atmospheric turbulence mitigation
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Abstract (arXiv)
A variety of neural networks architectures are being studied to tackle blur in images and videos caused by a non-steady camera and objects being captured. In this paper, we present an overview of these existing networks and perform experiments to remove the blur caused by atmospheric turbulence. Our experiments aim to examine the reusability of existing networks and identify desirable aspects of the architecture in a system that is geared specifically towards atmospheric turbulence mitigation. We compare five different architectures, including a network trained in an end-to-end fashion, thereby removing the need for a stabilization step.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 85 references that are in the map. First posted on arXiv 28 Oct 2024.
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References · 8
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
- 2016Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurringcited by 727
- 2019DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Bettercited by 362
- 2018Scale-recurrent Network for Deep Image Deblurringcited by 333
- 2020Cascaded Deep Video Deblurring Using Temporal Sharpness Priorcited by 85
- 2020Image Reconstruction of Static and Dynamic Scenes through Anisoplanatic Turbulencecited by 30
- 2020Deblurring using Analysis-Synthesis Networks Paircited by 29
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