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<p>Underwater image enhancement using dehazing approach represents significant research challenges in the field of underwater imaging and computer vision due to the effect of light absorption, scattering and wavelength dependent attenuation. This result in reduced contrast, color distortion, and blurred image, thus making it harder to observe, monitor and navigate underwater imaging. This research provides a comprehensive analysis of underwater image dehazing, describing the evolution of underwater image dehazing from traditional physics-based and enhancement-based techniques to advanced deep learning and transformer-based techniques. The work reviews the fundamental optical principles, such as Beer–Lambert equation and the Jaffe–McGlamery imaging model, which explain the concept of light wave propagation and its attenuation in underwater environments. Traditional enhancement-based models, such as histogram equalization, contrast-limited adaptive histogram equalization, retinex-based, and multi-scale fusion techniques, are efficient and have lower computational complexity. Recent deep learning-based models, comprising convolutional neural networks, generative adversarial networks, and transformer-based architectures, restore fine details, enhance perceptual quality, and improve generalisation under varied environmental conditions. This comprehensive review also highlights the performance assessment of existing benchmark datasets and image quality measurement parameters such as peak signal-to-noise ratio, structural similarity index, underwater image quality measure, and underwater color image quality evaluation. It also states various underwater image enhancement challenges such as changing environmental effect, data limitations and restrictions on real-time execution. It additionally glances at real time applications in robotics, underwater archaeology and marine biology which are based on emerging trends like physics-guided learning,, self-supervised training, lightweight architectures and domain adaptation.</p>

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From Physics to Transformers: A Review of Physics-Guided and Learning-Based Underwater Image Dehazing

  • 期刊论文

  • 发表日期：

    2026年4月29日

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  • Surbhi Sakshi,
  • Ashish Kumar Bhandari

摘要

Underwater image enhancement using dehazing approach represents significant research challenges in the field of underwater imaging and computer vision due to the effect of light absorption, scattering and wavelength dependent attenuation. This result in reduced contrast, color distortion, and blurred image, thus making it harder to observe, monitor and navigate underwater imaging. This research provides a comprehensive analysis of underwater image dehazing, describing the evolution of underwater image dehazing from traditional physics-based and enhancement-based techniques to advanced deep learning and transformer-based techniques. The work reviews the fundamental optical principles, such as Beer–Lambert equation and the Jaffe–McGlamery imaging model, which explain the concept of light wave propagation and its attenuation in underwater environments. Traditional enhancement-based models, such as histogram equalization, contrast-limited adaptive histogram equalization, retinex-based, and multi-scale fusion techniques, are efficient and have lower computational complexity. Recent deep learning-based models, comprising convolutional neural networks, generative adversarial networks, and transformer-based architectures, restore fine details, enhance perceptual quality, and improve generalisation under varied environmental conditions. This comprehensive review also highlights the performance assessment of existing benchmark datasets and image quality measurement parameters such as peak signal-to-noise ratio, structural similarity index, underwater image quality measure, and underwater color image quality evaluation. It also states various underwater image enhancement challenges such as changing environmental effect, data limitations and restrictions on real-time execution. It additionally glances at real time applications in robotics, underwater archaeology and marine biology which are based on emerging trends like physics-guided learning,, self-supervised training, lightweight architectures and domain adaptation.