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Paper: Dual-domain Mean-reverting Diffusion Model-enhanced Temporal Compressive Coherent Diffraction Imaging(DMDTC)

Authors:Hao Li, Jinwei Xu, Xinyi Wu, Cong Wan, Weisheng Xu, Jianghao Xiong, Wenbo Wan*, Qiegen Liu*, Senior Member, IEEE

Optics Express [Paper]

Date: Apr-22-2024

Version:1.0

The code and algorithm are for non-comercial use only.

Copyright 2024, School of Information Engineering, Nanchang University.

Abstract

Temporal compressive coherent diffraction imaging is a lensless imaging technique with the capability to capture fast-moving small objects. However, the accuracy of imaging reconstruction is often hindered by the loss of frequency domain information, a critical factor limiting the quality of the reconstructed images. To improve the quality of these reconstructed images, a method dual-domain mean-reverting diffusion model-enhanced temporal compressive coherent diffraction imaging (DMDTC) has been introduced. DMDTC leverages the mean-reverting diffusion model to acquire prior information in both frequency and spatial domain through sample learning. The frequency domain mean-reverting diffusion model is employed to recover missing information, while hybrid input-output algorithm is carried out to reconstruct the spatial domain image. The spatial domain mean-reverting diffusion model is utilized for denoising and image restoration. DMDTC has demonstrated a significant enhancement in the quality of the reconstructed images. The results indicate that the structural similarity and peak signal-to-noise ratio of images reconstructed by DMDTC surpass those obtained through conventional methods. DMDTC enables high time frame rates and high spatial resolution in coherent diffraction imaging.

Main procedure

Main procedure

Requirements and Dependencies

einops==0.6.0
lmdb==1.3.0
lpips==0.1.4
numpy==1.23.5
opencv-python==4.6.0.66
Pillow==9.3.0
PyYAML==6.0
scipy==1.9.3
tensorboardX==2.5.1
timm==0.6.12
torch==1.13.0
torchsummaryX==1.3.0
torchvision==0.14.0
tqdm
gradio

Checkpoints

We provide the pre-trained model. Click pre-trained model to download the pre-trained model.(Extraction code: DMDT)

Dataset

We provide the training dataset. Click datasets to download the dateset for training in our paper.(Extraction code: DMDT)

Training

Before start to training, the config file needs modifiction. The config path is Code/prior_learning/config/deblurring/options/train/ir-sde.yml.

Once you have modified the config file, run the following code to train your own model

python train.py -opt=options/train/ir-sde.yml

Reconstruction

Before conducting reconstruction, a pre-trained model or self-trained model is needed. Config file (whose path is Code/prior_learning/config/deblurring/options/test/ir-sde.yml) is needed to be modified for the model.

First in Code/Time_domain_unfolding, run python test.py to decompress a sapshot into multiple frames.

To supplement the frequency domain information, in path Code/prior_learning/config/deblurring run python test.py -opt=options/test/ir-sde.yml.

Then run python Code/HIO-DNN/PR_HIO_FFDNet.py to obtain the spatial domain images. After that, simply run again python test.py -opt=options/test/ir-sde.yml to obtain the final results.(Change the pre-trained model from frequency to spatial domain)

Acknowledgement

Thanks to these repositories for providing us with method code and experimental data: https://github.com/Algolzw/image-restoration-sde , https://github.com/zsm1211/TC-CDI

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