syqtju/THUholoLab-CCTV-phase-retrieval

A constrained complex total variation denoising algorithm with application to phase retrieval

Compressive phase retrieval via constrained complex total variation regularization (CCTV)

Authors: Yunhui Gao (gyh21@mails.tsinghua.edu.cn) and Liangcai Cao (clc@tsinghua.edu.cn)

Figure 1. (a) Schematic of the in-line holographic imaging system. (b) Captured raw hologram of a transparent Fresnel zone plate. Scale bar 1 mm. (c) Retrieved phase distribution. (d) Rendered surface height profile.

Requirements

Matlab 2019a or newer. Older visions may be sufficient but have not been tested.

Usage

• Phase retrieval using simulated data. Run demo_sim.m with default parameters.
• Phase retrieval using experimental data. First follow the instruction here to download the data. Then run demo_exp.m with default parameters.
• Try on your own experiment data. Prepare a hologram and an optional reference image, run preprocessing.m and set the experiment parameters (e.g. pixel size, wavelength, and sample-to-sensor distance). Then run demo_exp.m and see how it works.

Theories and References

For algorithm derivation and implementation details, please refer to our paper:

• Yunhui Gao and Liangcai Cao, "Iterative projection meets sparsity regularization: towards practical single-shot quantitative phase imaging with in-line holography," Light: Advanced Manufacturing 4, 6 (2023).