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Unsupervised water scene dehazing network using multiple scattering model.

Shunmin An1, Xixia Huang1, Linling Wang2

  • 1Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai, People's Republic of China.

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|June 28, 2021
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Summary
This summary is machine-generated.

This study introduces an unsupervised deep learning network for water scene dehazing, effectively addressing challenges with multiple scattering and limited datasets. The novel approach enhances image clarity for applications like marine vision and autonomous navigation.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • Water scenes present unique dehazing challenges due to multiple scattering and difficulty in acquiring ideal datasets.
  • Existing dehazing methods often struggle with the complexities of underwater and marine environments.

Purpose of the Study:

  • To propose an unsupervised deep learning network for effective dehazing in water scenes.
  • To overcome limitations of traditional methods by incorporating an atmospheric multiple scattering model and addressing dataset acquisition issues.

Main Methods:

  • Developed an unsupervised neural network integrating an atmospheric multiple scattering model.
  • Employed a four-branch network to estimate scene radiation, transmission map, blur kernel, and atmospheric light.
  • Introduced unsupervised loss functions, including color attenuation energy loss and dark channel loss.

Main Results:

  • The proposed method effectively synthesizes hazy images from estimated layers, minimizing the difference with the input.
  • Demonstrated superior performance in recovering details, structure, and texture compared to five advanced dehazing methods on synthetic and real-world data.
  • The output scene radiation layer serves as the final dehazed image.

Conclusions:

  • The unsupervised network effectively dehazes water scenes by modeling atmospheric multiple scattering.
  • The method shows significant potential for improving vision-based systems in challenging marine, river, and lake environments.
  • This approach offers a robust solution for enhancing visibility in hazy aquatic conditions.