Blurred Data Generation (Foams) for NN Training

This example demonstrates how to generate blurred image data for training neural network models for deconvolution (deblurring). Foam phantoms from xdesign are used to generate the clean images.

import numpy as np

from scico import plot
from scico.flax.examples import load_foam1_blur_data
plot.config_notebook_plotting()

Read data from cache or generate if not available.

n = 3  # convolution kernel size
σ = 20.0 / 255  # noise level
psf = np.ones((n, n)) / (n * n)  # kernel

train_nimg = 416  # number of training images
test_nimg = 64  # number of testing images
nimg = train_nimg + test_nimg
output_size = 256  # image size

train_ds, test_ds = load_foam1_blur_data(
    train_nimg,
    test_nimg,
    output_size,
    psf,
    σ,
    verbose=True,
)

Platform                  :   gpu
Device count              :   8
Data generation    time[s]:    63.71
Blur generation    time[s]:     9.33
Storing data in path      :   ~/.cache/scico/examples/data
Set --training--          :   Size:   416
Set --testing --          :   Size:   64
Data range -- images --   :    Min:   0.00, Max: 1.00
Data range -- labels --   :    Min:   0.00, Max: 1.00

Plot randomly selected sample.

indx_tr = np.random.randint(0, train_nimg)
indx_te = np.random.randint(0, test_nimg)
fig, axes = plot.subplots(nrows=2, ncols=2, figsize=(7, 7))
plot.imview(
    train_ds["label"][indx_tr, ..., 0],
    title="Ground truth - Training Sample",
    fig=fig,
    ax=axes[0, 0],
)
plot.imview(
    train_ds["image"][indx_tr, ..., 0],
    title="Blurred Image - Training Sample",
    fig=fig,
    ax=axes[0, 1],
)
plot.imview(
    test_ds["label"][indx_te, ..., 0],
    title="Ground truth - Testing Sample",
    fig=fig,
    ax=axes[1, 0],
)
plot.imview(
    test_ds["image"][indx_te, ..., 0],
    title="Blurred Image - Testing Sample",
    fig=fig,
    ax=axes[1, 1],
)
fig.suptitle(r"Training and Testing samples")
fig.tight_layout()
fig.colorbar(
    axes[0, 1].get_images()[0],
    ax=axes,
    shrink=0.5,
    pad=0.05,
    label="Arbitrary Units",
)
fig.show()