Code and experiments from the paper Information-driven design of imaging systems. For detailed usage, see the documentation.
pip install encoding_information
There may be more setup required to get the correct versions of Jax/Flax, see:
https://github.com/Waller-Lab/EncodingInformation/blob/main/Installation_guide.md
from encoding_information.models import PixelCNN, PoissonNoiseModel
from encoding_information import estimate_information, extract_patches
# Load measurement data (N x H x W numpy array of images)
measurements = load_measurements()
# Split into training/test sets and extract patches
# Breaking large images into patches increases computational efficiency
# Test set is used to evaluate information estimates
patches = extract_patches(measurements[:-200], patch_size=16)
test_patches = extract_patches(measurements[-200:], patch_size=16)
# Initialize and fit model to training data
model = PixelCNN() # Also supports FullGaussianProcess, StationaryGaussianProcess
noise_model = PoissonNoiseModel()
model.fit(patches)
# Estimate information content with confidence bounds
# Error bars are calculated based on test set size
info, lower_bound, upper_bound = estimate_information(
model,
noise_model,
patches,
test_patches,
confidence_interval=0.95
)
print(f"Information: {info:.2f} ± {(upper_bound-lower_bound)/2:.2f} bits/pixel")We provide three models with different tradeoffs:
- PixelCNN: Most accurate estimates but slowest
- FullGaussianProcess: Fastest
- StationaryGaussianProcess: Intermediate speed; Best performance on limited data
For highest accuracy, train multiple models and select the one giving the lowest information estimate, as each provides an upper bound on the true information content.
https://encodinginformation.readthedocs.io/en/latest/
- Make a fork and clone the fork
git remote add upstream https://github.com/Waller-Lab/EncodingInformation.gitgit config pull.rebase false- Use pull requests to contribute
git pull upstream mainto pull latest updates from the main repo