Phase retrieval via conjugate gradient minimization in double-plane lensless holographic microscopy

Area of Science:

• Optics and Photonics
• Computational Imaging
• Microscopy

Background:

• Digital lensless holographic microscopy enables label-free imaging but requires accurate phase retrieval.
• Existing phase retrieval algorithms like Gerchberg-Saxton struggle with low-frequency components.

Purpose of the Study:

• To develop and validate a novel optimization-based phase retrieval method for digital lensless holographic microscopy.
• To improve the reconstruction accuracy, particularly for low-frequency phase information.

Main Methods:

• Phase retrieval is formulated as an optimization problem solved using gradient descent tools.
• The conjugate gradient method is employed for its efficient convergence and computational advantages.
• The method is validated through extensive simulations and experimental measurements.

Main Results:

• The proposed conjugate gradient method demonstrates superior performance compared to the Gerchberg-Saxton algorithm.
• Accurate reconstruction of problematic low-frequency phase components is achieved.
• The method shows robustness in both simulated and experimental holographic data.

Conclusions:

• Optimization-based phase retrieval using the conjugate gradient method is a powerful approach for digital lensless holographic microscopy.
• This technique enhances the quality and reliability of reconstructed phase information.
• The method offers a significant advancement for quantitative phase imaging applications.