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Photonic integrated interference imaging system based on front-end S-shaped microlens array and Con-DDPM.

Xiaoyan Pan, Zhikun Yang, Rui Jia

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    This study introduces an improved photonic imaging system using an S-shaped microlens array and a deep learning model. The system enhances ultraviolet imaging quality by reducing artifacts and improving sampling uniformity.

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    Area of Science:

    • Optics and Photonics
    • Image Processing
    • Machine Learning

    Background:

    • Photonic integrated interference imaging systems face challenges with uneven UV spatial frequency sampling.
    • Inverse Fourier transform (IFT) artifacts degrade image quality in these systems.

    Purpose of the Study:

    • To propose a novel imaging system that addresses uneven sampling and IFT artifacts.
    • To enhance the imaging quality of photonic integrated interference systems for applications like space-based telescopes.

    Main Methods:

    • Implementation of a front-end S-shaped microlens array for uniform UV spatial frequency sampling.
    • Application of a conditional denoising diffusion probabilistic model (Con-DDPM) for image reconstruction and artifact removal.

    Main Results:

    • The S-shaped microlens array improved average peak signal-to-noise ratio (PSNR) by ~5 dB and structure similarity index measure (SSIM) by 0.16.
    • The Con-DDPM algorithm further enhanced average PSNR by ~9 dB and SSIM by 0.38, effectively removing artifacts.
    • Significant improvements in overall imaging quality were achieved.

    Conclusions:

    • The proposed system effectively mitigates sampling issues and IFT artifacts in photonic imaging.
    • The combination of advanced optics and deep learning offers a robust solution for high-quality UV imaging.
    • This work provides a foundation for developing next-generation space-based telescopes with superior imaging capabilities.