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Phase diversity algorithm with high noise robust based on deep denoising convolutional neural network.

Dequan Li, Shuyan Xu, Dong Wang

    Optics Express
    |September 13, 2019
    PubMed
    Summary
    This summary is machine-generated.

    This study integrates deep denoising convolutional neural networks (DnCNNs) into phase diversity (PD) algorithms to enhance accuracy in noisy conditions. The improved PD algorithm significantly reduces phase estimation errors, boosting robustness for optical measurements.

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    Area of Science:

    • Optical metrology
    • Image processing
    • Machine learning applications in optics

    Background:

    • Wave-front phase estimation is crucial for optical system performance.
    • Traditional phase diversity (PD) algorithms suffer from reduced accuracy due to noise in in-focus and defocus images.
    • Gaussian white noise significantly degrades the solution accuracy of PD algorithms.

    Purpose of the Study:

    • To improve the robustness of the phase diversity (PD) algorithm to noise.
    • To enhance the accuracy of wave-front phase estimation using deep learning.
    • To introduce deep denoising convolutional neural networks (DnCNNs) for image preprocessing in PD algorithms.

    Main Methods:

    • Employed a maximum-likelihood approach for wave-front phase estimation using Zernike polynomials.
    • Integrated deep denoising convolutional neural networks (DnCNNs) for preprocessing in-focus and defocus images contaminated by Gaussian white noise.
    • Compared the performance of the composite PD algorithm with DnCNNs against the traditional PD algorithm using Root Mean Square Error (RMSE) and Structural Similarity Index Measure (SSIM).

    Main Results:

    • The composite PD algorithm incorporating DnCNNs demonstrated superior performance over the traditional PD algorithm in both RMSE of phase estimation and SSIM.
    • The mean RMSE of phase estimation was significantly reduced by 78.48%, 82.35%, 71.09%, and 73.67% compared to the traditional PD algorithm.
    • The well-trained DnCNNs processed images rapidly, without increasing the overall running time of the PD algorithm.

    Conclusions:

    • The integration of DnCNNs into PD algorithms effectively enhances robustness against noise and improves phase estimation accuracy.
    • The proposed composite PD algorithm offers a significant advancement for optical measurements in noisy environments.
    • This approach holds potential for wide application in areas like intrinsic aberration measurements in optical systems and atmospheric turbulence compensation.