Align-Free Multi-Plane Phase Retrieval

TL;DR

The paper tackles misalignment challenges in multi-plane phase retrieval by proposing an alignment-free Adaptive Cascade Calibrated (ACC) pipeline. ACC combines autofocusing, adaptive cascade calibration (via SIFT-based feature matching and an object-space affine cascade with $H_n = H_{n-1} \cdot h_n$), and an energy-conserving Gerchberg-Saxton phase retrieval using three measurements. The approach eliminates the need for markers or precise hardware alignment, reduces diffraction-related issues during calibration, and automates the process end-to-end. Across simulations and optical experiments with biological samples, ACC demonstrates robust correction of misalignment and superior reconstruction quality (higher PSNR/SSIM) compared with naive methods, indicating meaningful cost and setup simplifications for practical computational-imaging applications.

Abstract

The multi-plane phase retrieval method provides a budget-friendly and effective way to perform phase imaging, yet it often encounters alignment challenges due to shifts along the optical axis in experiments. Traditional methods, such as employing beamsplitters instead of mechanical stage movements or adjusting focus using tunable light sources, add complexity to the setup required for multi-plane phase retrieval. Attempts to address these issues computationally face difficulties due to the variable impact of diffraction, which renders conventional homography techniques inadequate. In our research, we introduce a novel Adaptive Cascade Calibrated (ACC) strategy for multi-plane phase retrieval that overcomes misalignment issues. This technique detects feature points within the refocused sample space and calculates the transformation matrix for neighboring planes on-the-fly to digitally adjust measurements, facilitating alignment-free multi-plane phase retrieval. This approach not only avoids the need for complex and expensive optical hardware but also simplifies the imaging setup, reducing overall costs. The effectiveness of our method is validated through simulations and real-world optical experiments.

Figures (10)

• Figure 1: Two prevalent causes of experimental errors. The left panel illustrates error cause (a), while the right panel depicts error cause (b).
• Figure 2: Feature point detection between the measurements.
• Figure 3: Schematic diagram of multi-plane phase retrieval algorithm based on adaptive cascade calibrated algorithm.
• Figure 4: Measurements with and without simulated misalignment.
• Figure 5: Numerical Experiment Comparison: ACC vs. Naiv̈e Methods.