Denoising¶

The goal is to remove noise from an image. Our library includes Noise2Void [KBJ19] using any of the U-Net versions provided. The main advantage of Noise2Void is neither relying on noise image pairs nor clean target images since frequently clean images are simply unavailable.

Input:
- Noisy image.
Output:
- Image without noise.

In the figure below an example of this workflow’s input is depicted:

Input image. Obtained from Noise2Void project.¶

Data preparation¶

To ensure the proper operation of the library the data directory tree should be something like this:

dataset/
├── train
│   └── x
│       ├── training-0001.tif
│       ├── training-0002.tif
│       ├── . . .
│       ├── training-9999.tif
└── test
    └── x
        ├── testing-0001.tif
        ├── testing-0002.tif
        ├── . . .
        ├── testing-9999.tif

Configuration file¶

Find in templates/denoising folder of BiaPy a few YAML configuration templates for this workflow.

Special workflow configuration¶

Please refer to Noise2Void to understand the method functionality. These variables can be set:

PROBLEM.DENOISING.N2V_PERC_PIX controls the percentage of pixels per input patch to be manipulated. This is the n2v_perc_pix in their code.

PROBLEM.DENOISING.N2V_MANIPULATOR controls how the pixels will be replaced. This is the n2v_manipulator in their code.

PROBLEM.DENOISING.N2V_NEIGHBORHOOD_RADIUS controls the radius of the neighborhood. This is the n2v_neighborhood_radius in their code.

PROBLEM.DENOISING.N2V_STRUCTMASK wheter to use Struct Noise2Void.

Run¶

Jupyter notebooks: run via Google Colab

2D:
3D:

Command line: Open a terminal as described in Installation. For instance, using n2v_2d_denoising.yaml template file, the code can be run as follows:

# Configuration file
job_cfg_file=/home/user/n2v_2d_denoising.yaml
# Where the experiment output directory should be created
result_dir=/home/user/exp_results
# Just a name for the job
job_name=n2v_2d_denoising
# Number that should be increased when one need to run the same job multiple times (reproducibility)
job_counter=1
# Number of the GPU to run the job in (according to 'nvidia-smi' command)
gpu_number=0

# Move where BiaPy installation resides
cd BiaPy

# Load the environment
conda activate BiaPy_env

python -u main.py \
       --config $job_cfg_file \
       --result_dir $result_dir  \
       --name $job_name    \
       --run_id $job_counter  \
       --gpu $gpu_number

Docker: Open a terminal as described in Installation. For instance, using n2v_2d_denoising.yaml template file, the code can be run as follows:

# Configuration file
job_cfg_file=/home/user/n2v_2d_denoising.yaml
# Path to the data directory
data_dir=/home/user/data
# Where the experiment output directory should be created
result_dir=/home/user/exp_results
# Just a name for the job
job_name=n2v_2d_denoising
# Number that should be increased when one need to run the same job multiple times (reproducibility)
job_counter=1
# Number of the GPU to run the job in (according to 'nvidia-smi' command)
gpu_number=0

docker run --rm \
    --gpus "device=$gpu_number" \
    --mount type=bind,source=$job_cfg_file,target=$job_cfg_file \
    --mount type=bind,source=$result_dir,target=$result_dir \
    --mount type=bind,source=$data_dir,target=$data_dir \
    danifranco/biapy \
        -cfg $job_cfg_file \
        -rdir $result_dir \
        -name $job_name \
        -rid $job_counter \
        -gpu $gpu_number

Note

Note that data_dir must contain all the paths DATA.*.PATH and DATA.*.MASK_PATH so the container can find them. For instance, if you want to only train in this example DATA.TRAIN.PATH and DATA.TRAIN.MASK_PATH could be /home/user/data/train/x and /home/user/data/train/y respectively.

Results¶

The results are placed in results folder under --result_dir directory with the --name given. An example of this workflow is depicted below:

Example of denoising model prediction.¶

Following the example, you should see that the directory /home/user/exp_results/n2v_2d_denoising has been created. If the same experiment is run 5 times, varying --run_id argument only, you should find the following directory tree:

n2v_2d_denoising/
├── config_files/
│   └── n2v_2d_denoising.yaml
├── checkpoints
|   ├── model_weights_n2v_2d_denoising_1.h5
|   ├── normalization_mean_value.npy
│   └── normalization_std_value.npy
└── results
    ├── n2v_2d_denoising_1
    ├── . . .
    └── n2v_2d_denoising_5
        ├── cell_counter.csv
        ├── aug
        │   └── .tif files
        ├── charts
        │   ├── n2v_2d_denoising_1_n2v_mse.png
        │   ├── n2v_2d_denoising_1_loss.png
        │   └── model_plot_n2v_2d_denoising_1.png
        └── per_image
            └── .tif files

config_files: directory where the .yaml filed used in the experiment is stored.
- n2v_2d_denoising.yaml: YAML configuration file used (it will be overwrited every time the code is run).
checkpoints: directory where model’s weights are stored.
- model_weights_n2v_2d_denoising_1.h5: model’s weights file.
- normalization_mean_value.npy: normalization mean value. Is saved to not calculate it everytime and to use it in inference.
- normalization_std_value.npy: normalization std value. Is saved to not calculate it everytime and to use it in inference.
results: directory where all the generated checks and results will be stored. There, one folder per each run are going to be placed.
- n2v_2d_denoising_1: run 1 experiment folder.
  cell_counter.csv: file with a counter of detected objects for each test sample.
  
  aug: image augmentation samples.
  
  charts:
  
  n2v_2d_denoising_1_n2v_mse.png: Noise2Void MSE over epochs plot (when training is done).
  
  n2v_2d_denoising_1_loss.png: Loss over epochs plot (when training is done).
  
  model_plot_n2v_2d_denoising_1.png: plot of the model.
  
  per_image:
  
  .tif files: reconstructed images from patches.

Note

Here, for visualization purposes, only n2v_2d_denoising_1 has been described but n2v_2d_denoising_2, n2v_2d_denoising_3, n2v_2d_denoising_4 and n2v_2d_denoising_5 will follow the same structure.