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A Patch-Based Method for Underwater Image Enhancement With Denoising Diffusion Models.

Haisheng Xia, Binglei Bao, Fei Liao

    IEEE Transactions on Cybernetics
    |October 30, 2024
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    This study introduces a new method using denoising diffusion models to enhance blurry and color-distorted underwater images, improving marine research clarity.

    Area of Science:

    • Marine Biology
    • Computer Vision
    • Image Processing

    Background:

    • Underwater images suffer from blurriness and color distortion due to light scattering and absorption.
    • These degradations hinder marine research and exploration tasks.
    • Existing image enhancement techniques often fall short in addressing these specific challenges.

    Purpose of the Study:

    • To propose a novel approach for enhancing degraded underwater images.
    • To leverage denoising diffusion models for improved image quality.
    • To enable high-resolution image generation while preserving natural details.

    Main Methods:

    • Utilized denoising diffusion models (DDMs) for underwater image enhancement.
    • Trained the noise estimation network within the DDMs.

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  • Accelerated the sampling process using denoising diffusion implicit models (DDIMs).
  • Implemented a patch-based method with overlapping average sampling for arbitrary resolution generation.

    • Main Results:

    • The proposed method demonstrates superior or comparable performance against state-of-the-art techniques.
    • Benchmark experiments validate the effectiveness and efficiency of the approach.
    • Salient object detection experiments confirm reduced interference from underwater environments.

    Conclusions:

    • The novel DDM-based approach effectively enhances underwater images.
    • The method preserves natural appearance and details at arbitrary resolutions.
    • This advancement significantly aids in extracting semantic information from underwater imagery for marine research.