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    Area of Science:

    • Computational imaging
    • Artificial intelligence in optics
    • Machine learning for scientific imaging

    Background:

    • Lensless computational imaging combines optical measurements with algorithms.
    • Traditional methods require costly supervised learning due to ill-posed measurements.
    • Artificial neural networks offer new possibilities for lensless imaging.

    Purpose of the Study:

    • To develop a self-supervised learning method for lensless imaging.
    • To learn semantic representations from implicitly provided priors.
    • To overcome the limitations of costly supervised approaches in lensless imaging.

    Main Methods:

    • A self-supervised learning approach using a contrastive loss function.
    • Training a target extractor (measurements) from a source extractor (natural scenes).
    • Transferring cross-modal priors into a shared latent space.

    Main Results:

    • The method effectively classifies mask-modulated scenes on unseen datasets.
    • Achieved comparable accuracy to the contrastive language-image pre-trained (CLIP) network.
    • Demonstrated the transferability of priors from structured natural scenes to modulated scenes.

    Conclusions:

    • The proposed method avoids costly data annotation for lensless imaging.
    • It offers greater adaptability to unseen data compared to conventional techniques.
    • The multimodal representation learning is applicable to various downstream vision tasks in unconventional imaging settings.