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During leveling, the Earth's curvature and atmospheric refraction introduce deviations in the line of sight from a true horizontal reference. When the line of sight is leveled, it remains perpendicular to the plumb line only at a single point. Beyond this, it deviates due to the Earth’s curvature, represented by the correction C. For a sight distance D, the deviation can be derived using the relationship:This relationship shows that the deviation increases quadratically with distance. Over a...
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To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead to...
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Light rays enter the eye through the cornea, a transparent dome-shaped tissue that is the eye's outermost layer. The cornea bends or refracts, light rays traveling to the pupil. The shape of the cornea determines how much of the light is bent and whether the image will be focused correctly on the retina at the back of the eye. Once the light has passed through both refraction layers, it converges into a single focal point onto a small area. This is where photoreceptors start transforming...
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Simple and efficient deviation correction framework for multi-height lensless imaging.

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    Summary
    This summary is machine-generated.

    This study introduces a computational lensless imaging framework that uses adaptive optical calibration and quantitative phase retrieval. It achieves robust, high-quality imaging by automatically correcting alignment errors, reducing costs and complexity.

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    Area of Science:

    • Optics
    • Computational Imaging
    • Biomedical Engineering

    Background:

    • Lensless imaging offers high-throughput imaging but is sensitive to axial and radial displacement errors.
    • Conventional methods require expensive precision devices and complex optical alignment, limiting accessibility and speed.

    Purpose of the Study:

    • To develop a cost-effective and rapid lensless imaging framework.
    • To enable robust, high-quality complex-amplitude imaging by integrating adaptive optical calibration and quantitative phase retrieval.
    • To eliminate the need for precision positioning devices and complex alignment procedures.

    Main Methods:

    • Developed a computational framework integrating adaptive optical calibration and quantitative phase retrieval.
    • Implemented iterative optimization of sub-diffraction pattern coordinates for automatic alignment.
    • Utilized Structural Similarity Index minimization with partitioned interval strategy and stochastic gradient descent for axial displacement error calibration.

    Main Results:

    • Achieved sub-pixel radial localization accuracy and axial distance correction errors under 1.05%.
    • Demonstrated robust image reconstruction across various specimens with coupled multi-parameter deviations.
    • Maintained high-quality complex-amplitude imaging even under extreme conditions with rough mechanical positioning.

    Conclusions:

    • The proposed computational framework significantly simplifies lensless imaging system design and reduces hardware costs.
    • Adaptive optical calibration and quantitative phase retrieval eliminate the need for demanding physical alignment and high-precision devices.
    • Enables high-throughput, cost-effective, and robust lensless imaging for diverse applications.