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Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture
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Direct wavefront sensing with a plenoptic sensor based on deep learning.

Hao Chen, Haobo Zhang, Yi He

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    |May 9, 2023
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    Summary

    This study introduces a novel deep learning approach for wavefront sensing using plenoptic wavefront sensors (PWS). The method significantly improves phase retrieval accuracy, outperforming existing techniques.

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    Area of Science:

    • Optics and Photonics
    • Artificial Intelligence
    • Wavefront Sensing

    Background:

    • Traditional plenoptic wavefront sensors (PWS) exhibit limitations in phase retrieval due to step changes in slope response.
    • Existing wavefront sensing methods struggle with non-linear problems inherent in PWS.

    Purpose of the Study:

    • To develop a direct wavefront restoration method for PWS using a novel deep learning model.
    • To overcome the performance limitations of traditional PWS phase retrieval.

    Main Methods:

    • A neural network model integrating transformer and U-Net architectures was employed.
    • The model directly restores wavefront from PWS plenoptic images.

    Main Results:

    • Achieved an averaged root mean square error (RMSE) of residual wavefront below 1/14λ (Marechal criterion).
    • Demonstrated superior performance compared to recent deep learning models and traditional modal approaches.
    • Validated model robustness against varying turbulence strength and signal levels, indicating good generalizability.

    Conclusions:

    • The proposed deep learning method successfully addresses the non-linear challenges in PWS wavefront sensing.
    • This represents the first deep learning-based direct wavefront detection in PWS applications, achieving state-of-the-art results.