Experimental study demonstrates reduced parameter count and preserved accuracy in MNIST image classification using Haar wavelet decomposition, highlighting efficiency gains for resource-limited CNNs.
This study explores integrating Haar wavelet decomposition techniques with convolutional neural networks for image classification on the MNIST dataset. The research demonstrates that without losing significant accuracy by applying the 1-level, 2-level, and 3-level decomposition techniques, the model can reduce the dimensionality and the number of parameters required by the convolutional neural network model. During the training, the 1-level Haar CNN results achieved optimal performance, demonstrating competitive accuracy and computational efficiency compared to the baseline CNN model. This approach highlights the potential of wavelet decomposition techniques to enhance CNN performance with limited computational resources.
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Ismayilov et al. (2025) studied this question.
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