Example 02: Apply trained network for regression

This script applies a trained MS-D network for regression (i.e. denoising/artifact removal) Run generatedata.py first to generate required training data and train_regr.py to train a network.

# Import code
import msdnet
import glob
import tifffile
import os

# Make folder for output
os.makedirs('results', exist_ok=True)

# Load network from file
n = msdnet.network.MSDNet.from_file('regr_params.h5', gpu=True)

# Process all test images
flsin = sorted(glob.glob('test/noisy/*.tiff'))
for i in range(len(flsin)):
    # Create datapoint with only input image
    d = msdnet.data.ImageFileDataPoint(flsin[i])
    # Compute network output
    output = n.forward(d.input)
    # Save network output to file
    tifffile.imsave('results/regr_{:05d}.tiff'.format(i), output[0])