Denoising¶

The denoising module reduces noise in cryo-electron tomograms while preserving structural detail. It is typically the first step in a CryoSiam pipeline, preceding semantic segmentation, instance segmentation, or particle identification.

For best performance, it is recommended to use WBP-reconstructed tomograms, as the denoising model was trained on simulated WBP data. Since the implementation of WBP reconstruction in AreTomo3 is different from the IMOD implementation, there is additional model supporting WBP reconstruction from AreTomo3.

Left: Raw WBP noisy tomogram
Middle: Gaussian filtered tomogram
Right: CryoSiam denoised tomogram

Trained Model¶

A pre-trained denoising model is provided and can be downloaded from:

CryoSiam denoising model (v1.0)

CryoSiam denoising model - AreTomo3 supported (v1.0)

Running denoising¶

Denoising is performed via the command-line interface using a YAML configuration file:

cryosiam denoise_predict --config_file=configs/config_denoising.yaml

What this command does

Loads the trained denoising model and the input tomogram(s)
Applies sliding-window 3D inference (GPU)
Writes denoised tomograms to the specified output directory
Optionally saves intermediate or raw prediction files

Visualization Denoised outputs can be visualized using CryoSiam-Vis. See the visualization instructions here.

Example Configuration File¶

Download example config

data_folder: '/scratch/stojanov/dataset1/tomograms'
prediction_folder: '/scratch/stojanov/dataset1/predictions/denoised'

trained_model: '/scratch/stojanov/trained_models/cryosiam_denoising.ckpt'
file_extension: '.mrc'

test_files: null

save_raw_predictions: False
scale_prediction: True

parameters:
  data:
    patch_size: [ 128, 128, 128 ]
    min: 0
    max: 1
    mean: 0
    std: 1
  network:
    in_channels: 1
    spatial_dims: 3

hyper_parameters:
  batch_size: 3

Config Reference¶

Top‑level keys¶

Key	Type	Must change the default value	Description
`data_folder`	`str`	✅	Directory containing input tomograms to denoise
`prediction_folder`	`str`	✅	Output directory for denoised tomograms
`trained_model`	`str`	✅	Path to the trained denoising model checkpoint (`.ckpt`)
`file_extension`	`str`	❌	Input file extension (`.mrc` or `.rec`, default: `.mrc`)
`test_files`	`list[str]` or `null`	❌	List of specific filenames to process. Use `null` to process all files in `data_folder`
`save_raw_predictions`	`bool`	❌	Save raw prediction files (.h5) in addition to final outputs
`scale_prediction`	`bool`	❌	Rescale output intensities to [0, 1]

`parameters`¶

Key	Type	Must change the default value	Description
`data.patch_size`	`list[int]`	❌	Sliding-window patch size for 3D inference
`data.min`	`float`	❌	Intensity minimum value for data scaling
`data.max`	`float`	❌	Intensity maximum value for data scaling
`data.mean`	`float`	❌	Mean used for normalization
`data.std`	`float`	❌	Std used for normalization
`network.in_channels`	`int`	❌	Number of input channels (usually `1`)
`network.spatial_dims`	`int`	❌	Dimensionality of the model (`3` for tomograms)

Tips
Keep patch_size consistent with the value used during training (e.g. 128³ for the provided model). Reduce it only if you encounter GPU memory issues.

`hyper_parameters`¶

Key	Type	Must change the default value	Description
`batch_size`	`int`	❌	Number of 3D patches processed per forward pass.

Outputs¶

The denoising step produces:

Denoised tomograms saved to prediction_folder (file extension as implemented).
Optional raw prediction files if save_raw_predictions: true.

Output filenames follow the input basenames with appropriate suffixes.

Troubleshooting¶

Symptom	Suggested Fix
`CUDA out of memory`	Reduce `batch_size` and/or `patch_size`; ensure no other heavy GPU processes are running.
No files processed	Ensure `data_folder` exists and `test_files` is `null` or matches filenames exactly.
Output intensities look off	Toggle `scale_prediction`.
Model/shape mismatch	Verify `in_channels` and `spatial_dims` and the correct `.ckpt` file are used.

Next Steps¶

After denoising, continue with:

For an overview of complete pipelines, see the Usage overview.