Two noise-robust axial scanning multi-image phase retrieval algorithms based on Pauta criterion and smoothness...

Area of Science:

• Optics and Photonics
• Computational Imaging
• Signal Processing

Background:

• Iterative phase retrieval algorithms are crucial for coherent diffraction imaging but are sensitive to noise.
• Existing methods struggle with high noise levels, limiting practical applications.
• Conventional iteration indicators can be unreliable in experimental settings.

Purpose of the Study:

• To develop noise-robust iterative methods for phase retrieval and diffractive imaging.
• To enhance retrieval accuracy and convergence speed under noisy conditions.
• To introduce a more reliable metric for evaluating retrieval performance in experiments.

Main Methods:

• Proposed two novel iterative algorithms incorporating the Pauta criterion and smoothness constraint.
• Focused on exploiting longitudinal diversity for improved phase retrieval.
• Introduced and validated a new retrieval metric to overcome limitations of conventional indicators.

Main Results:

• Demonstrated superior retrieval accuracy and faster convergence at high shot noise levels through numerical and experimental analyses.
• Showcased the algorithms' robustness against various noise types.
• Verified the effectiveness of the novel retrieval metric in practical scenarios.

Conclusions:

• The proposed noise-robust iterative methods significantly improve phase retrieval and diffractive imaging performance.
• The new retrieval metric offers a more reliable evaluation tool for experimental applications.
• This work is expected to broaden the applicability of iterative multi-image phase retrieval techniques.