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    Area of Science:

    • Optics and Photonics
    • Computer Vision
    • Artificial Intelligence

    Background:

    • Wrapped phase patterns are crucial in optical metrology but often suffer from uneven gray values and speckle noise due to material properties and measurement conditions.
    • Existing deep learning methods may struggle to capture the full range of gray value variations and noise characteristics in complex phase patterns.

    Purpose of the Study:

    • To develop an improved deep learning model for simultaneous restoration of uneven gray values and elimination of speckle noise in wrapped phase patterns.
    • To create a novel dataset specifically for training and evaluating models on challenging uneven and noisy phase data.
    • To enhance the performance of the dilated-blocks-based deep convolution neural network (DBDNet) for phase pattern analysis.

    Main Methods:

    • An improved dilated-blocks-based deep convolution neural network (DBDNet) was developed with enhanced dilated blocks to capture multi-scale information.
    • A new dataset was curated comprising computer-simulated and experimentally obtained uneven, noisy wrapped phase patterns from dynamic measurements.
    • A combined MS_SSIM+L1 loss function was employed to improve both denoising and restoration accuracy.

    Main Results:

    • The proposed improved DBDNet demonstrated superior performance in reducing speckle noise and restoring uneven gray values compared to ResNet-based, ADNet, and BRDNet.
    • Ablation studies confirmed the effectiveness of the improved model structure and the combined loss function.
    • Quantitative and qualitative evaluations validated the method's ability to handle complex, real-world phase data.

    Conclusions:

    • The enhanced DBDNet effectively addresses the challenges of uneven gray values and speckle noise in wrapped phase patterns.
    • The proposed method offers a significant advancement in phase pattern restoration for optical metrology and dynamic measurements.
    • The developed dataset and improved network provide a valuable resource for future research in phase retrieval and image processing.