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Related Concept Videos

Vision01:24

Vision

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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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Visual System01:26

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Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
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Position detection of elements in off-axis three-mirror space optical system based on ResNet50 and LSTM.

Deyan Zhu, Chengcheng Li, Yongqi Ao

    Optics Express
    |January 29, 2025
    PubMed
    Summary

    This study introduces a novel ResNet50-LSTM method for precise element position detection in off-axis optical systems. The approach achieves high accuracy and efficiency, saving time and resources in optical system adjustments.

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    Area of Science:

    • Optical Engineering
    • Machine Learning Applications in Optics

    Background:

    • Accurate element positioning is critical for maintaining imaging quality in complex off-axis three-mirror space optical systems.
    • Existing methods for element position detection often face challenges in achieving both high precision and efficiency.

    Purpose of the Study:

    • To propose and evaluate a novel method for high-precision and high-efficiency position detection of elements in off-axis three-mirror space optical systems.
    • To leverage deep learning techniques for improved performance in optical element metrology.

    Main Methods:

    • Utilized the ResNet50 convolutional neural network to extract high-dimensional feature vectors from point spread functions (PSFs) across different fields of view for efficient feature extraction.
    • Employed a Long Short-Term Memory (LSTM) network to process these feature vectors, capturing positional information across different image planes for enhanced precision.
    • Evaluated the method through three distinct position detection scenarios: single-dimensional component variation, multi-dimensional random variation, and detector variation.

    Main Results:

    • Achieved 100% detection accuracy better than 10 µm for eccentricity in single-dimensional and detector variation scenarios.
    • Reached 94% detection accuracy better than 10 arcseconds for tilt in multi-dimensional random variation scenarios.
    • Demonstrated that the method obtains element positions with a single calculation, closely approximating final results and significantly reducing adjustment time and resources.

    Conclusions:

    • The proposed ResNet50-LSTM method offers an accurate and effective solution for element position detection in off-axis three-mirror space optical systems.
    • This deep learning-based approach enhances both the precision and efficiency of optical element metrology, contributing to improved imaging quality and streamlined system alignment.