Noisy Data Generation for NN Training

This example demonstrates how to generate noisy image data for training neural network models for denoising. The original images are part of the BSDS500 dataset provided by the Berkeley Segmentation Dataset and Benchmark project.

import numpy as np

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

Read data from cache or generate if not available.

size = 40  # patch size
train_nimg = 400  # number of training images
test_nimg = 64  # number of testing images
nimg = train_nimg + test_nimg
gray = True  # use gray scale images
data_mode = "dn"  # Denoising problem
noise_level = 0.1  # Standard deviation of noise
noise_range = False  # Use fixed noise level
stride = 23  # Stride to sample multiple patches from each image

train_ds, test_ds = load_image_data(
    train_nimg,
    test_nimg,
    size,
    gray,
    data_mode,
    verbose=True,
    noise_level=noise_level,
    noise_range=noise_range,
    stride=stride,
)

Downloading BSR_bsds500.tgz from http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/BSR/
Download complete
Extracting content from tar file BSR_bsds500.tgz
Read 400 images for training
Read 100 images for testing
Saving as ~/.cache/scico/examples/data/BSDS/bsds500.npz
Storing data in path      :   ~/.cache/scico/examples/data
Set --training--          :   Size:   104000
Set --testing --          :   Size:   16640
Data range -- images --   :    Min:   0.00, Max: 1.00
Data range -- labels --   :    Min:   0.00, Max: 1.00

Plot randomly selected sample. Note that patches have small sizes, thus, plots may correspond to unidentifiable fragments.

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="Noisy 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="Noisy 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,
)
fig.show()