We trained a Conditional Variational Diffusion Model (CVDM) (G. Della Maggiora, 2023) using the BioSR dataset (Qiao & Li, 2020) (DOI: 10.6084/m9.figshare.13264793.v9). To evaluate if the model can generate natural images similar to the gtound truth, we assessed the Naturalness Factor(Gong, Y., & Sbalzarini, I. F. 2014) at two stages: prior learning (before training) and post-processing (after applying the model's output enhancements).
We trained a Conditional **Variational Diffusion Model (CVDM)** (G. Della Maggiora, 2023) using the **BioSR dataset** (Qiao & Li, 2020) (DOI: 10.6084/m9.figshare.13264793.v9). To evaluate if the model can generate natural images similar to the gtound truth, we assessed the **Naturalness Factor**(Gong, Y., & Sbalzarini, I. F. 2014) at two stages: prior learning (before training) and post-processing (after applying the model's output enhancements).
The model is trained for 10 epochs with a batch size of 2. Overall, the model is trained for 500,000 iterations, with generation time step T = 200, learning rate 0.0001.
During theinference, generation time steps are set to T = 500. The naturalization of images is donewith the ImageJ Mosaic Suite plugin.
The implementation of CVDM can be found on the GitHub page (G. Della Maggiora, L. A. Croquevielle, N. Deshpande, H. Horsley, T. Heinis, A. Yaki-movich, Conditional variational diffusion models, https://github.com/casus/cvdm)2023.
The Naturalness Factor information and documentation (including a guide on installation of the Mosaic Suite plugin) can be found in MOSAIC group, MosaicSuite documentation, https://sbalzarinilab.org/MosaicSuiteDoc/index.html.
##How to use the images?
Every folder contains 100 TIFF images representing four biological structures from the **BioSR dataset**, 25 images for each structure.
-**CCP** (Clathrin-Coated Pits)
-**ER** (Endoplasmic Reticulum)
-**F-actin** (Filamentous Actin)
-**MT** (Microtubules)
The **BioSR_low_resolution_images** folder contains low-resolution images.
The **BioSR_high_resolution_images** folder contains the high-resolution grond truth images.
The **naturalized_BioSR_high_resolution_images** folder contains the naturalized high-resolution grond truth images, which are obtained by naturalize the images in **BioSR_high_resolution_images** using ImageJ Mosaic Suite plugin.
The **CVDM_inference** folder contains CVDM generated images during the inference time with images from **BioSR_low_resolution_images** as input.
