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An end-to-end sea fog removal network using multiple scattering model.

Shunmin An1, Xixia Huang1, Zhangjing Zheng1

  • 1Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai, China.

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|May 14, 2021
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Summary

This study introduces an advanced sea fog removal network that utilizes a multiple scattering model to enhance image clarity. The novel approach effectively removes dense fog, outperforming existing methods in both quantitative and qualitative evaluations.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Atmospheric Optics

Background:

  • Sea fog significantly degrades image quality, particularly in oceanic scenes.
  • Existing single scattering models fail to adequately address the complexities of dense fog.
  • Image blurring and artifacts are common challenges in fog removal algorithms.

Purpose of the Study:

  • To propose an end-to-end deep learning network for effective sea fog removal.
  • To leverage a multiple scattering model for more accurate fog density representation.
  • To improve image quality by mitigating blurring and artifacts.

Main Methods:

  • Developed an end-to-end network incorporating a re-formulated atmospheric multiple scattering model.
  • Unified transmission map, atmospheric light, and blur kernel into a single formula.
  • Employed smooth dilation, sub-pixel techniques, and a multi-scale sub-network.
  • Integrated multiple loss functions for comprehensive network training.

Main Results:

  • The proposed model effectively removes sea fog, producing clearer images.
  • Quantitative and qualitative experimental results demonstrate superiority over state-of-the-art methods.
  • The multiple scattering model significantly reduces image blurring compared to single scattering models.
  • Advanced techniques successfully avoided gridding and halo artifacts.

Conclusions:

  • The developed network provides a robust solution for sea fog removal.
  • The multiple scattering model is crucial for handling dense fog scenarios, especially in ocean environments.
  • The integration of advanced techniques and multiple loss functions enhances overall performance and image fidelity.