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Visual-quality-driven unsupervised image dehazing.

Aiping Yang1, Yumeng Liu2, Jinbin Wang2

  • 1School of Electrical and Information Engineering, Tianjin University, Tianjin, China; Shanghai Artificial Intelligence Laboratory, China.

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Summary
This summary is machine-generated.

This study introduces a new lightweight unsupervised dehazing network that removes haze from images without needing paired clean images. The method uses an interactive fusion module and iterative optimization, achieving results comparable to supervised methods.

Keywords:
Image dehazingInteractive fusionIterative enhancementUnsupervised learningVisual-quality-driven

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Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • Existing learning-based dehazing methods often require large paired datasets of hazy and clean images, which are difficult to acquire.
  • Training on synthetic data can lead to a domain shift when applied to real-world hazy images.
  • There is a need for effective unsupervised dehazing techniques that do not rely on paired data.

Purpose of the Study:

  • To propose a novel, lightweight, unsupervised dehazing network capable of directly predicting clear images from hazy inputs.
  • To address the limitations of existing methods concerning data acquisition and domain shift.
  • To develop a robust dehazing solution that performs well on real-world images without reference data.

Main Methods:

  • The proposed network incorporates an interactive fusion module (IFM) to combine multi-level features and an iterative optimization module (IOM) for refining dehazed results.
  • Four non-reference, visual-quality-driven loss functions (dark channel loss, contrast loss, saturation loss, edge sharpness loss) are designed to facilitate unsupervised training.
  • The method operates directly on hazy images, eliminating the need for corresponding clean images.

Main Results:

  • Extensive experiments were conducted on both synthetic and real-world datasets.
  • The proposed unsupervised method demonstrated favorable performance compared to state-of-the-art unsupervised dehazing techniques.
  • The method achieved performance comparable to some supervised dehazing methods, as indicated by metrics like Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Universal Image Quality Index (UQI).

Conclusions:

  • The developed lightweight unsupervised dehazing network effectively removes haze without requiring paired training data.
  • The novel approach, utilizing IFM, IOM, and quality-driven loss functions, overcomes the limitations of synthetic data training and domain shift issues.
  • The method shows significant potential for real-world applications requiring efficient and accurate image dehazing.