LUCYD: A Feature-Driven Richardson-Lucy Deconvolution Network

[paper][arXiv]

Run in Colab 🟡▶️

The process of acquiring microscopic images in life sciences often results in image degradation and corruption, characterised by the presence of noise and blur, which poses significant challenges in accurately analysing and interpreting the obtained data. We propse LUCYD, a novel method for the restoration of volumetric microscopy images that combines the Richardson-Lucy deconvolution formula and the fusion of deep features obtained by a fully convolutional network. By integrating the image formation process into a feature-driven restoration model, the proposed approach aims to enhance the quality of the restored images whilst reducing computational costs and maintaining a high degree of interpretability.

Prerequisities:

• Python 3.7 or higher
• PyTorch 1.12.1 or higher
• NumPy 1.22.4
• TorchMetrics 1.0.1
• Tifffile 2023.7.10

Training:

model = LUCYD(num_res=1)
model = train(model, train_dataloader, test_dataloader)

Testing:

evaluate(model, eval_dataloader)

Cite

Please cite our work if you find it useful to your research.

@InProceedings{10.1007/978-3-031-43993-3_63,
  author="Chobola, Tom{\'a}{\v{s}}
  and M{\"u}ller, Gesine
  and Dausmann, Veit
  and Theileis, Anton
  and Taucher, Jan
  and Huisken, Jan
  and Peng, Tingying",
  title="LUCYD: A Feature-Driven Richardson-Lucy Deconvolution Network",
  booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2023",
  year="2023",
  publisher="Springer Nature Switzerland",
  pages="656--665",
  isbn="978-3-031-43993-3"
}