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Learning mapping by curve iteration estimation For real-time underwater image enhancement

Optics Express +

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April 4, 2024

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April 4, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces a lightweight framework for underwater image enhancement, improving visual quality and enabling real-time performance. The novel approach reduces computational demands, making it ideal for practical applications and small devices.

Area Of Science

  • Computer Vision
  • Image Processing

Background

  • Underwater image quality is degraded by light attenuation and scattering.
  • Existing enhancement algorithms often lack real-time performance and are computationally intensive.

Purpose Of The Study

  • To develop a lightweight and efficient framework for underwater image enhancement.
  • To overcome the limitations of current algorithms in practical underwater vision tasks.

Main Methods

  • A novel iterative curve estimation approach is proposed to learn image mappings.
  • A parameter estimation network, CieNet, is utilized to learn curve parameters.
  • A set of loss functions guides the parameter learning process.

Main Results

  • The proposed method achieves superior performance over existing algorithms in quantitative metrics and visual quality.
  • The lightweight network design significantly reduces computational resource requirements.
  • The method demonstrates extremely short running times, facilitating real-time applications.

Conclusions

  • The developed framework offers an effective and efficient solution for underwater image enhancement.
  • Its lightweight nature and real-time capabilities make it highly applicable for integration into various devices.
  • This research advances the field of underwater computer vision by providing a practical and performant enhancement technique.