What question did this study set out to answer?

To compare the convergence behavior and practical performance of iterative phase retrieval algorithms and gradient search methods using intensity measurements under physical constraints.

August 1, 1982Applied Optics

Phase retrieval algorithms: a comparison

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Authors

JFJames R. FienupUniversity of Rochester

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Implication

Computational analysis demonstrates faster convergence for input-output and conjugate-gradient algorithms in optical phase retrieval, indicating more efficient image reconstruction.

Key Points

To compare the convergence behavior and practical performance of iterative phase retrieval algorithms and gradient search methods using intensity measurements under physical constraints.
Evaluated iterative phase reconstruction algorithms across single-intensity setups with non-negativity constraints (astronomy) and dual-intensity setups (electron microscopy and wavefront sensing).
Compared mathematical convergence and rate properties of the Gerchberg-Saxton and error-reduction algorithms directly against steepest-descent, input-output, and conjugate-gradient methods.
Demonstrated that both the Gerchberg-Saxton algorithm for dual measurements and the error-reduction algorithm for single measurements converge reliably, with error reduction closely mirroring steepest descent.
Showed through numerical examples that the input-output algorithm and conjugate-gradient method achieve substantially faster practical convergence than standard error reduction.

Cite This Study

James R. Fienup (1982) studied this question.

synapsesocial.com/papers/6a02b37998cafe0df57552ec https://doi.org/10.1364/ao.21.002758

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August 1, 1982Applied Optics

Phase retrieval algorithms: a comparison

View Full Paper

Ask AI

Bookmark

Authors

JFJames R. FienupUniversity of Rochester

Discussion

Member takes

Implication

Computational analysis demonstrates faster convergence for input-output and conjugate-gradient algorithms in optical phase retrieval, indicating more efficient image reconstruction.

Key Points

To compare the convergence behavior and practical performance of iterative phase retrieval algorithms and gradient search methods using intensity measurements under physical constraints.
Evaluated iterative phase reconstruction algorithms across single-intensity setups with non-negativity constraints (astronomy) and dual-intensity setups (electron microscopy and wavefront sensing).
Compared mathematical convergence and rate properties of the Gerchberg-Saxton and error-reduction algorithms directly against steepest-descent, input-output, and conjugate-gradient methods.
Demonstrated that both the Gerchberg-Saxton algorithm for dual measurements and the error-reduction algorithm for single measurements converge reliably, with error reduction closely mirroring steepest descent.
Showed through numerical examples that the input-output algorithm and conjugate-gradient method achieve substantially faster practical convergence than standard error reduction.

Cite This Study

James R. Fienup (1982) studied this question.

synapsesocial.com/papers/6a02b37998cafe0df57552ec https://doi.org/10.1364/ao.21.002758

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