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    Area of Science:

    • Optical imaging
    • Digital image processing
    • Machine learning

    Background:

    • Traditional deconvolution methods struggle with unknown optical transfer functions (OTF) and require extensive preprocessing.
    • Defocus aberration significantly degrades image quality in optical systems.
    • Phase masks (PM) offer a method for encoding information in optical systems.

    Purpose of the Study:

    • To propose and validate an optical-digital imaging system for blind deconvolution using Jacobi-Fourier profile functions and a convolutional neural network (CNN).
    • To demonstrate the system's ability to recover high-frequency image details under defocus conditions without prior knowledge of the optical transfer function (OTF).
    • To evaluate the system's performance on untrained encoded images and its robustness against variations in optical parameters.

    Main Methods:

    • Image encoding using point spread functions (PSF) derived from cubic and Jacobi-Fourier polynomial (JFP) phase masks (PM) with radial orders p=7, 9, and 10.
    • Training a blind deconvolution convolutional neural network (CNN) on simulated encoded image datasets with added defocus aberration.
    • Applying the trained CNN to both simulated and experimental optical-digital imaging data, including images encoded with PMs not present in the training set.

    Main Results:

    • The trained CNN successfully recovered high frequencies in images with varying defocus values, outperforming traditional methods.
    • The system demonstrated effective blind deconvolution without requiring preprocessing steps like noise removal or radiometric normalization on experimental data.
    • The CNN decoded optically encoded images from untrained phase masks, showcasing adaptability and robustness.
    • The proposed CNN architecture achieved high-quality image recovery with minimal artifacts and enhanced contrast, even under significant defocus.

    Conclusions:

    • The proposed optical-digital imaging system with a CNN-based blind deconvolution network offers a robust solution for image recovery under defocus.
    • The system's ability to handle untrained optical conditions and eliminate the need for preprocessing makes it highly practical for real-world applications.
    • This approach provides a computationally efficient method for improving image quality in optical systems without precise knowledge of the optical transfer function (OTF).