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Denoising

The denoising module reduces noise in tomograms while preserving structural detail. It’s typically the first step before semantic/instance segmentation or particle identification. For best performance, it's recommended to have a WBP reconstructed tomogram, since the denoising model was trained on WBP simulated tomograms.

Noisy vs Denoised Tomogram

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

Trained Model

You can download the trained model from here: CryoSiam denoising model (v1.0)

Command

Run denoising via the CLI using a YAML configuration file:

cryosiam denoise_predict --config_file=configs/config_denoising.yaml

What it does

  • Loads the trained denoising model and your tomogram/s
  • Applies sliding‑window 3D inference (GPU/CPU)
  • Writes denoised volumes to the output folder (and optional intermediates)

Visualization For visualization of the results, CryoSiam-Vis can be used as described here.

Example Configuration File

Download example config

data_folder: '/scratch/stojanov/datatset1/tomograms'
log_dir: '/scratch/stojanov/datatset1/'
prediction_folder: '/scratch/stojanov/datatset1/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:
  gpu_devices: 1
  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.
log_dir str ✅ Folder where logs (runtime, metrics, debug files) are written.
prediction_folder str ✅ Output directory where denoised tomograms (and optional intermediates) are saved.
trained_model str ✅ Path to the denoising trained model checkpoint file (e.g., .ckpt).
file_extension str ❌ Extension of input tomograms (.mrc or .rec). Default is .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 ❌ If true, also save the raw .h5 file/s.
scale_prediction bool ❌ If true, rescale/restore prediction to intensity 0 to 1.

parameters

Key Type Must change the default value Description
gpu_devices int or list[int] ❌ Is there a GPU(s) to use. Example: 1 or [0]. Set to [0] to force CPU inference (not recommended).
data.patch_size list[int] ❌ 3D sliding‑window patch size, e.g., [128,128,128]. Reduce if you hit GPU OOM error, otherwise the default is what was use for training the models.
data.min float ❌ Intensity floor applied before normalization/clipping.
data.max float ❌ Intensity ceiling applied before normalization/clipping.
data.mean float ❌ Mean used for normalization (match training stats if model expects it).
data.std float ❌ Std used for normalization (match training stats if model expects it).
network.in_channels int ❌ Number of input channels (typically 1 for tomograms).
network.spatial_dims int ❌ Dimensionality of the model (use 3 for 3D tomograms).

Tips
• Keep patch_size the same value as used in the trained network (e.g., the available trained networks are with patch size 128).

hyper_parameters

Key Type Must change the default value Description
batch_size int ❌ Number of 3D patches per forward pass. Increase for throughput; decrease if you hit GPU memory limits.

Outputs

  • Denoised tomograms written to prediction_folder (file extension as implemented).
  • Optional raw outputs if save_raw_predictions: true.
  • Logs saved under log_dir (progress, timings, optional debug artifacts).

Naming: outputs follow the input basenames with an appropriate suffix/extension as implemented in CryoSiam.

Troubleshooting

Symptom Suggested Fix
CUDA out of memory Lower hyper_parameters.batch_size and/or parameters.data.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; adjust data.min/max or remove custom mean/std to match training normalization.
Checkpoint/shape mismatch Verify network.in_channels and spatial_dims match the trained model; confirm you’re using the correct .ckpt.

Next Steps