PadDH:Physics-aware diffusion model

This repository contains the code for the paper
Zhang, Yunping, Xihui Liu, and Edmund Y. Lam. "Single-shot inline holography using a physics-aware diffusion model." Optics Express 32.6 (2024): 10444-10460.

In this paper, we propose a novel unsupervised algorithm called physics-aware diffusion model (PadDH), specifically designed for snapshot digital holographic reconstruction. PadDH represents a pioneering adaptation of the diffusion model that seamlessly integrates the physical model into its generative process. Through comprehensive experiments using both synthetic and experimental data, we validate the remarkable capabilities of PadDH in effectively reducing twin-image contamination and generating high-quality reconstructions. Our work showcases significant advancements in holographic imaging by harnessing the powerful prior knowledge encoded in pre-trained diffusion models, leading to improved reconstruction quality and enhanced efficiency from single-shot measurements.

If you have any question on the code, please contact the author: yp000925@connect.hku.hk

Requirements

• Python 3
• Pytorch 1.11.0
• cudatoolkit 11.3.1
• Pillow 9.0.1
• GPU memory >= 10G

Usage

Reproduce the results in the paper

• Download the pre-trained diffusion model from the open-source model card. In our experiment, we use the model trained on class-unconditional ImageNet: 256x256_diffusion_uncond.pt
• Update the model path in configuration file configs/config.yaml
• The simulation verification has been done on two datasets: data/cells and data/USAF. The results will be saved in the folder ./results by default.
  • Run python simulation.py --task_config configs/task_sim_USAF.yaml to reproduce the simulation results of USAF target in the paper.
  • Run python simulation.py --task_config configs/task_sim_cell.yaml to reproduce the simulation results of cells target in the paper.
• The experimental verification has been done on two datasets: ExpSample/USAF and ExpSample/convallaria. The results will be saved in the folder ./results by default.
  • Run python experiment.py --task_config configs/task_exp.yaml --exp_name USAF to reproduce the experimental results of USAF target in the paper.
  • Run python experiment.py --task_config configs/task_exp.yaml --exp_name convallaria to reproduce the experimental results of convallaria sample in the paper.

Try your own dataset

The following is a simple process to try your own dataset.

• Download the pre-trained diffusion model from the open-source model card. In our experiment, we use the model trained on class-unconditional ImageNet: 256x256_diffusion_uncond.pt
• Update the model path in configuration file configs/config.yaml
• Prepare your own dataset. The dataset should be saved in the folder ./data/YOUR_DATA. The dataset should be saved in the format of .png or .jpg.
• Fill in the parameters of your setup in configs/task_config.yaml
• Run python main.py --task_config configs/task_exp.yaml --exp_name YOUR_EXP_NAME --save_dir YOUR_OUTPATH to reconstruct your own data. The results will be saved in the folder ./YOUR_OUTPATH.

Citatation

If you find this work useful, please consider citing the following paper:

@article{zhang2024single,
  title={Single-shot inline holography using a physics-aware diffusion model},
  author={Zhang, Yunping and Liu, Xihui and Lam, Edmund Y},
  journal={Optics Express},
  volume={32},
  number={6},
  pages={10444--10460},
  year={2024},
  publisher={Optica Publishing Group}
}

Ackowledgements

This implementation is based on / inspired by the open-source diffusion models DDPM, guided-diffusion, DPS