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  3. Image-to-image Bayesian Flow Networks With Structurally Informative Priors.

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Image-to-Image Bayesian Flow Networks With Structurally Informative Priors.

Hongkun Dou, Jinyang Du, Xingyu Jiang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |July 30, 2025

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    Image-to-Image Bayesian Flow Networks (I2I-BFNs) enable efficient image-to-image translation by operating in distribution parameter spaces. This novel framework adapts generative models for tasks beyond noise-to-image generation.

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    Area of Science:

    • Artificial Intelligence
    • Computer Vision
    • Machine Learning

    Background:

    • Diffusion models excel at image generation but struggle with non-random inputs.
    • Real-world tasks like image restoration require adapting models to specific data distributions.

    Purpose of the Study:

    • Introduce Image-to-Image Bayesian Flow Networks (I2I-BFNs) for general-purpose image-to-image translation.
    • Develop a framework that effectively handles non-random input distributions for generative models.

    Main Methods:

    • Operates within the parameter space of distributions, modeling pixel intensities with Gaussian distributions.
    • Employs closed-form Bayesian inference to refine distribution parameters, guided by network predictions.
    • Utilizes the conditional image as a prior parameter for deterministic initialization, reducing variance.
  • Introduces a skip sampling technique for enhanced translation efficiency.

    • Main Results:

    • I2I-BFNs demonstrate competitive performance across various image restoration and translation tasks.
    • The framework effectively adapts generative models to non-random input distributions.
    • Experimental evaluations confirm the model's efficacy and adaptability in diverse settings.

    Conclusions:

    • I2I-BFNs offer a novel and efficient approach to conditional image generation.
    • This work provides new opportunities for developing large-scale, efficient conditional generation systems.
    • The framework enhances the adaptability of generative models for practical image-to-image translation applications.