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Retrieval01:12

Retrieval

Retrieval is the process of getting information out of memory storage and back into conscious awareness. This ability is essential for daily tasks like brushing hair and teeth, driving to work, and performing job duties. Retrieval occurs in three ways: recall, recognition, and relearning.
Recall involves accessing information without cues, such as during an essay test, where individuals must retrieve facts and concepts from memory unaided. Another example is remembering the name of a colleague...

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Related Experiment Video

Updated: Jun 9, 2026

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Phase-retrieval algorithms for a complicated optical system.

J R Fienup

    Applied Optics
    |September 8, 2010
    PubMed
    Summary
    This summary is machine-generated.

    New phase-retrieval algorithms accelerate optical system characterization. These advanced methods accurately reconstruct complex optical systems using point spread functions, even with detector imperfections.

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    Last Updated: Jun 9, 2026

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    Area of Science:

    • Optics and Photonics
    • Computational Imaging

    Background:

    • Phase-retrieval algorithms are crucial for characterizing optical systems.
    • Existing methods struggle with complex systems involving multiple Fresnel propagations, aperture absorption, and detector noise.

    Purpose of the Study:

    • To develop advanced phase-retrieval algorithms for complex optical systems.
    • To improve the accuracy and computational efficiency of optical system characterization.

    Main Methods:

    • Derivation of gradient-search and generalized iterative-transform phase-retrieval algorithms.
    • Calculation of analytic gradient expressions for error metrics.
    • Development of algorithms independent of coefficient or phase point count for faster computation.

    Main Results:

    • Algorithms handle multiple Fresnel-like transforms, aperture absorption, and bad detector pixels.
    • Gradient computation is significantly accelerated, requiring only forward and backward wave propagation transforms.
    • Convergence proof for the generalized iterative transform algorithm is provided.
    • Reconstruction of pupil amplitude is achieved.

    Conclusions:

    • The developed algorithms enable more accurate characterization of complicated optical systems.
    • These methods offer significant computational speed-up for phase retrieval.
    • The algorithms are robust to system complexities like absorption and detector noise.