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Moireé Pattern Detection using Wavelet Decomposition and Convolutional Neural Network

Moiré patterns are interference patterns that are produced due to the overlap of the digital grids of the camera sensor resulting in a high-frequency noise in the image. This paper proposes a new method to detect Moiré patterns using wavelet decomposition and a multi-input deep Convolutional Neural Network (CNN), for images captured from a computer screen. Also, this paper proposes a method to use the normalized intensity values in the image, as weights for the frequency strength of Moiré pattern. The CNN model created with this approach is robust to high background frequencies other than those of Moiré patterns, as the model is trained using images captured considering diverse scenarios. We have tested this model in receipt scanning application, to detect the Moiré patterns produced in the images captured from a computer screen, and achieved an accuracy of 98.4%.

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