WGDG_demo

Description: Lensless imaging reconstruction code using Wirtinger gradient descent optimization.

Title: Matlab code for "Wirtinger gradient descent optimization for reducing Gaussian noise in lensless microscopy"
Author: You Zhou (zhouyou@nju.edu.cn), Xia Hua, Zibang Zhang and Xuemei Hu, etc.
Version: 1.0
Copyright: 2019, You Zhou, Xia Hua, Zibang Zhang, Xuemei Hu, Krishna Dixit, Jingang Zhong, Guoan Zheng, Xun Cao*.

Matlab code for "Wirtinger gradient descent optimization for reducing Gaussian noise in lensless microscopy"

This package contains an implementation of Wirtinger gradient descent optimization based lensless imaging algorithm described in the paper: You Zhou, Xia Hua, Zibang Zhang and Xuemei Hu, etc., "Wirtinger gradient descent optimization for reducing Gaussian noise in lensless microscopy".

Please cite our paper if using the code in an academic publication.

For algorithmic details, please refer to our paper: https://www.sciencedirect.com/science/article/pii/S0143816620300671?via%3Dihub

How to use

The code is tested in MATLAB 2014b and 2018a (64bit) under the MS Windows 10 64bit version with Intel Core i7-8700 CPU @ 3.20GHz and 32GB RAM.

1. Unpack the package.
2. Include code and subdirectory in your Matlab path.
3. Run "main.m" to test the demo included in this package.

Some illustrations

1. The folder GenerateIlluminationPattern includes codes to generate illumination patterns.
2. The folder GenerateRawImageSet includes codes to generate raw image set in differet situations.
3. getRec_GS() is the GS reconstruction function, and getRec_WGDG() is the proposed WGDG reconstruction function.
4. getRoundMatrixMask() generates the pinhole mask matrix in use, and prop() realizes the free-space propagation of light field.
5. The derivative process of our algorithm uses both the vector or matrix forms. But the final updating equations of WF algorithm are all represented in matrices for more efficient calculation and easier code realization (like by MATLAB).

Parameters setting

We realize both GS and WGDG algorithms of lensless imaging with mask updating in the demo. The GS algorithm here is based on the references [1][2].

1. The GS method will reduce the contrast of recovered image with the existance of noise, so a contrast enhancement process (set CEflag = 1) is always needed to show the result. The proposed WGDG method does not have this problem. However, for fair performance comparison, the images shown in the paper all have adjusted their contrast using the 'Adjust the brightness and contrast' function and 'Auto' button of ImageJ.
2. For WGDG reconstruction, the Delta parameters (gradient descent step sizes) are improtant, which can be set according to the equations in the paper, but always need careful adjustment. In fact, these parameters can be set to some constant values empirically. For example, we can simply set the Delta parameters of object, pinhole mask, and noise matrix to 0.5, 0.008, and 0.002 respectively.

Important notes

If you have any questions regarding this code, please contact You Zhou (zhouyou@nju.edu.cn).

References of GS algorithm for lensless imaging:

1. A. M. Maiden and J. M. Rodenburg, “An improved ptychographical phase retrieval algorithm for diffractive imaging,” Ultramicroscopy 109, 1256–1262 (2009).
2. Z. Zhang, Y. Zhou, S. Jiang, K. Guo, K. Hoshino, J. Zhong, J. Suo, Q. Dai, and G. Zheng, “Invited article: Mask-modulated lensless imaging with multi-angle illuminations,” APL Photonics 3, 060803 (2018).