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Anisotropic regularization for sparsely sampled and noise-robust Fourier ptychography.

Kyung Chul Lee, Hyesuk Chae, Shiqi Xu

|November 14, 2024

Summary

This summary is machine-generated.

Fourier ptychography (FP) reconstruction is improved with a new algorithm using anisotropic total variation and Tikhonov regularizations. This method enhances image quality under noisy and sparse data conditions, crucial for high-throughput imaging.

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Area of Science:

Computational imaging
Microscopy
Image reconstruction

Background:

Fourier ptychography (FP) offers super-resolution and quantitative phase imaging.
FP reconstruction is ill-posed with noisy or insufficient data.
High-throughput imaging demands robust FP reconstruction.

Purpose of the Study:

To develop a regularized FP reconstruction algorithm for improved performance under challenging conditions.
To enhance image quality and data recovery in sparse and noisy FP measurements.

Main Methods:

Proposed a regularized FP reconstruction algorithm.
Utilized anisotropic total variation (TV) for object function regularization.
Employed Tikhonov regularization for pupil function.
Formulated reconstruction using the alternating direction method of multipliers (ADMM).

Main Results:

Successfully recovered high-quality images from sparsely sampled and noisy measurements.
Demonstrated effective noise suppression and edge preservation with TV regularization.
Retrieved accurate amplitude and phase images under insufficient sampling.
Outperformed other FP reconstruction algorithms under harsh conditions.

Conclusions:

The proposed regularized FP algorithm significantly improves reconstruction quality.
Anisotropic TV and Tikhonov regularizations are effective for noisy and sparse FP data.
The method is validated on real experimental FP microscopy images, showing practical utility.