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.

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.

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

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

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

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

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)

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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