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Phase retrieval from noisy data based on minimization of penalized I-divergence
Kerkil Choi1, Aaron D Lanterman
1School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA. kerkil@mtu.edu
Summary
This study addresses noise in phase retrieval using Csiszár's I-divergence. Penalized methods effectively suppress noise artifacts, improving image reconstruction accuracy in applications like astronomy and crystallography.
Area of Science:
- Image reconstruction
- Information theory
- Computational imaging
Background:
- Phase retrieval is crucial for reconstructing images from intensity measurements.
- Noise artifacts, particularly Poisson noise, degrade image quality in phase retrieval.
- Csiszár's I-divergence is an information-theoretic measure used for discrepancy minimization.
Purpose of the Study:
- To investigate and quantify noise artifacts in phase retrieval using Csiszár's I-divergence.
- To develop and evaluate methods for noise suppression in phase retrieval.
- To adapt computational techniques for efficient noise-penalized phase retrieval.
Main Methods:
- Simulating Poisson noise in image autocorrelations and Fourier magnitudes.
- Quantifying noise effects using error metrics and signal-to-noise ratios.
- Applying penalized minimum I-divergence methods with Green's one-step-late approach.
Main Results:
- Characterization of noise artifact impact across varying signal-to-noise ratios.
- Demonstration of noise suppression capabilities of the proposed penalized methods.
- Successful adaptation of Green's one-step-late for computational efficiency.
Conclusions:
- Penalized minimum I-divergence methods offer effective noise suppression in phase retrieval.
- The adapted computational framework enables practical application of these noise reduction techniques.
- This work enhances image quality in fields relying on phase retrieval, such as astronomy and crystallography.
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