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    This study introduces an end-to-end design for single lens imaging systems, optimizing lens and restoration parameters simultaneously using deep learning. This approach creates high-quality, wide field-of-view (FoV) imaging systems with reduced complexity.

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    Area of Science:

    • Optics and Photonics
    • Computer Vision
    • Machine Learning

    Background:

    • Traditional imaging systems often use complex, multi-lens designs optimized for metrics like modulation transfer function and field-of-view (FoV).
    • This complexity increases size, weight, and cost, limiting applications in compact optoelectronic devices.

    Purpose of the Study:

    • To develop an end-to-end design methodology for single lens imaging systems.
    • To reduce the complexity of imaging systems by integrating lens design and image restoration.
    • To achieve high-quality imaging with a wide field-of-view using a simplified optical configuration.

    Main Methods:

    • Established an imaging and processing model using a Res-Unet architecture for image restoration.
    • Developed a differentiable ray tracing model to enable end-to-end optimization.
    • Simultaneously optimized lens surface parameters and restoration algorithm parameters via deep learning, targeting minimal image restoration error.

    Main Results:

    • Successfully designed a single lens imaging system with a high-quality, wide field-of-view (47°).
    • Demonstrated the feasibility of an integrated, end-to-end approach for optical system design.
    • Achieved significant complexity reduction compared to traditional multi-lens designs.

    Conclusions:

    • The proposed end-to-end deep learning method enables efficient design of simplified, high-performance single lens imaging systems.
    • This approach offers a promising pathway for developing compact and lightweight optoelectronic imaging solutions.
    • The differentiable ray tracing model is crucial for enabling simultaneous optimization of optical and computational components.