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BiBBDM: Bidirectional Image Translation With Brownian Bridge Diffusion Models.

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    This study introduces the Bidirectional Brownian Bridge Diffusion Model (BiBBDM) for efficient image-to-image translation. BiBBDM enables simultaneous bidirectional translation using a single model, reducing computational costs.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Traditional image-to-image translation methods often require separate models for each translation direction, leading to computational inefficiency.
    • Existing Diffusion Models (DMs) typically handle translation as a unidirectional conditional generation process.

    Purpose of the Study:

    • To introduce a novel approach for bidirectional image-to-image translation using a single model.
    • To leverage Brownian Bridge processes for simultaneous learning of translation between two domains.

    Main Methods:

    • The Bidirectional Brownian Bridge Diffusion Model (BiBBDM) is proposed, modeling translation as a stochastic Brownian Bridge process.
    • BiBBDM enables bidirectional translation through different sampling directions of a single model, avoiding the need for multiple models.

    Main Results:

    • BiBBDM achieves bidirectional image translation using a single model and parameters.
    • The method demonstrates competitive performance on various benchmarks, validated by visual and quantitative metrics.
    • This represents the first framework for simultaneous dual-domain sampling based on Brownian Bridge diffusion processes.

    Conclusions:

    • BiBBDM offers an efficient and effective solution for bidirectional image-to-image translation.
    • The use of Brownian Bridge diffusion processes facilitates simultaneous dual-domain sampling within a unified model.
    • The proposed model significantly reduces computational resource requirements compared to traditional methods.