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Diff-Retinex++: Retinex-Driven Reinforced Diffusion Model for Low-Light Image Enhancement.

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    Summary

    This study introduces Diff-Retinex++, a novel diffusion model for low-light image enhancement. It integrates Retinex theory with a Denoising Diffusion Model (DDM) and Mixture of Experts (MoE) for superior results.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Artificial Intelligence

    Background:

    • Low-light conditions cause significant image degradation, impacting visual quality and downstream tasks.
    • Existing low-light enhancement methods often struggle with noise, color distortion, and loss of detail.

    Purpose of the Study:

    • To develop a physically-constrained generative model for low-light image enhancement.
    • To address limitations of current methods by integrating diffusion models with Retinex theory.

    Main Methods:

    • Proposed Diff-Retinex++ model combining a Denoising Diffusion Model (DDM) and a Retinex-Driven Mixture of Experts (RMoE).
    • DDM handles enhancement as a generative task, leveraging powerful generation capabilities.
    • RMoE integrates Retinex theory via a plug-and-play attention module for feature regulation and knowledge distillation.

    Main Results:

    • Diff-Retinex++ achieves significant improvements in low-light image enhancement.
    • The model demonstrates effectiveness, superiority, and generalization on real-world datasets.
    • Qualitative and quantitative experiments validate the proposed approach.

    Conclusions:

    • The Retinex-driven reinforced diffusion model offers a pioneering approach to low-light image enhancement.
    • The integration of DDM and RMoE effectively balances generative vividness and restoration fidelity.
    • The proposed method shows strong potential for practical applications in image enhancement.