Evaluation of neural network algorithms for atmospheric turbulence mitigation

• URL: http://arxiv.org/abs/2410.20816v1
• Date: Mon, 28 Oct 2024 08:04:57 GMT
• Title: Evaluation of neural network algorithms for atmospheric turbulence mitigation
• Authors: Tushar Jain, Madeline Lubien, Jerome Gilles,
• Abstract summary: A variety of neural networks are being studied to tackle blur in images and videos caused by a non-steady camera and objects being captured. We present an overview of these existing networks and perform experiments to remove the blur caused by atmospheric turbulence.
• Score: 0.5461938536945721
• License:
• Abstract: 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.

Related papers

• Single Frame Atmospheric Turbulence Mitigation: A Benchmark Study and A New Physics-Inspired Transformer Model [82.23276183684001]
  We propose a physics-inspired transformer model for imaging through atmospheric turbulence. The proposed network utilizes the power of transformer blocks to jointly extract a dynamical turbulence distortion map. We present two new real-world turbulence datasets that allow for evaluation with both classical objective metrics and a new task-driven metric using text recognition accuracy.
  arXiv  Detail & Related papers  (2022-07-20T17:09:16Z)
• Learning to restore images degraded by atmospheric turbulence using uncertainty [93.72048616001064]
  Atmospheric turbulence can significantly degrade the quality of images acquired by long-range imaging systems. We propose a deep learning-based approach for restring a single image degraded by atmospheric turbulence.
  arXiv  Detail & Related papers  (2022-07-07T17:24:52Z)
• A comparison of different atmospheric turbulence simulation methods for image restoration [64.24948495708337]
  Atmospheric turbulence deteriorates the quality of images captured by long-range imaging systems. Various deep learning-based atmospheric turbulence mitigation methods have been proposed in the literature. We systematically evaluate the effectiveness of various turbulence simulation methods on image restoration.
  arXiv  Detail & Related papers  (2022-04-19T16:21:36Z)
• Self-Denoising Neural Networks for Few Shot Learning [66.38505903102373]
  We present a new training scheme that adds noise at multiple stages of an existing neural architecture while simultaneously learning to be robust to this added noise. This architecture, which we call a Self-Denoising Neural Network (SDNN), can be applied easily to most modern convolutional neural architectures.
  arXiv  Detail & Related papers  (2021-10-26T03:28:36Z)
• Evolving Architectures with Gradient Misalignment toward Low Adversarial Transferability [4.415977307120616]
  We propose an architecture searching framework that employs neuroevolution to evolve network architectures. Our experiments show that the proposed framework successfully discovers architectures that reduce transferability from four standard networks. In addition, the evolved networks trained with gradient misalignment exhibit significantly lower transferability compared to standard networks trained with gradient misalignment.
  arXiv  Detail & Related papers  (2021-09-13T12:41:53Z)
• Limited-angle tomographic reconstruction of dense layered objects by dynamical machine learning [68.9515120904028]
  Limited-angle tomography of strongly scattering quasi-transparent objects is a challenging, highly ill-posed problem. Regularizing priors are necessary to reduce artifacts by improving the condition of such problems. We devised a recurrent neural network (RNN) architecture with a novel split-convolutional gated recurrent unit (SC-GRU) as the building block.
  arXiv  Detail & Related papers  (2020-07-21T11:48:22Z)
• Learning to Restore a Single Face Image Degraded by Atmospheric Turbulence using CNNs [93.72048616001064]
  Images captured under such condition suffer from a combination of geometric deformation and space varying blur. We present a deep learning-based solution to the problem of restoring a turbulence-degraded face image.
  arXiv  Detail & Related papers  (2020-07-16T15:25:08Z)
• Evaluation, Tuning and Interpretation of Neural Networks for Meteorological Applications [0.030458514384586396]
  Neural networks have opened up many new opportunities to utilize remotely sensed images in meteorology. Common applications include image classification, e.g., to determine whether an image contains a tropical cyclone, and image translation, e.g., to emulate radar imagery for satellites that only have passive channels. This article highlights several strategies and practical considerations for neural network development that have not yet received much attention in the meteorological community.
  arXiv  Detail & Related papers  (2020-05-06T20:46:10Z)
• Deblurring using Analysis-Synthesis Networks Pair [6.09170287691728]
  Blind image deblurring remains a challenging problem for modern artificial neural networks. We propose a new architecture which breaks the deblurring network into an analysis network which estimates the blur, and a synthesis network that uses this kernel to deblur the image.
  arXiv  Detail & Related papers  (2020-04-06T19:32:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.