Moire Pattern Detection
Introduction
To detect Moire ́ patterns, images are first decomposed using Wavelet decomposition and trained using multi-input Convolutional neural network. The strength of the proposed CNN model is, it uses the LL intensity image (from the Wavelet decomposition) as a weight parameter for the Moire ́ pattern, thereby approximating the spatial spread of the Moire ́ pattern in the image. Usage of CNN model performs better than frequency thresholding approach as the model is trained considering diverse scenarios and it is able to distinguish between the high frequency of background texture and the Moire ́ pattern.
If this code helps with your work, please cite:
@INPROCEEDINGS{8628746,
author={E. {Abraham}},
booktitle={2018 IEEE Symposium Series on Computational Intelligence (SSCI)},
title={Moiré Pattern Detection using Wavelet Decomposition and Convolutional Neural Network},
year={2018},
volume={},
number={},
pages={1275-1279},
ISSN={},
month={Nov},}
Set-up
1. Install Python3
2. Install dependencies
pip install tensorflow
pip install keras
pip install Pillow
pip install scikit-learn
pip install scikit-image
pip install PyWavelets
Note - Resize Images to WIDTH = 1000 and HEIGHT = 750
3. create the wavelet decomposed training dataset from the captured images
python createTrainingData.py positiveImages negativeImages train
positional arguments:
positiveImages Directory with positive (Moiré pattern) images.
negativeImages Directory with negative (Normal) images.
train 0 = train, 1 = test
4. train the CNN model using training images
python train.py positiveImages negativeImages trainingDataPositive trainingDataNegative epochs
positional arguments:
positiveImages Directory with original positive (Moiré pattern)
images.
negativeImages Directory with original negative (Normal) images.
trainingDataPositive Directory with transformed positive (Moiré pattern)
images.
trainingDataNegative Directory with transformed negative (Normal) images.
epochs Number of epochs for training
5. test the CNN model using test images
python test.py moirePattern3CNN_.h5 positiveImages negativeImages
positional arguments:
weightsFile saved CNN model file
positiveTestImages Directory with original positive (Moiré pattern)
images.
negativeTestImages Directory with original negative (Normal) images.
Documentation
IEEE SSCI-2018 publication (https://ieeexplore.ieee.org/document/8628746).
Note: The paper shows 3 bands of wavelet decomposition images taken as input to CNN as compared to the 4 bands in this python implementation
Additional information
If you have any technical questions, feel free to contact us or create an issue here.