A comparative study between MLP and CNN for noise reduction on images : The impact of different input dataset sizes and the impact of different types of noise on performance
Abstract: Images damaged by noise present a problem that can be addressed by performing noise-reduction using neural networks. This thesis analyses the performance of two different neural networks, a Mulilayer Perceptron (MLP) and a Convolutional Neural Network (CNN), when performing noise reduction on images. Specifically focusing on the impact of the size of dataset used to train the two different kinds of neural networks has on the performance, as well as how well these two networks perform when reducing different types of noise. This in an attempt to determine whether the use of the more modern type of network, the CNN, performs better than the older type of network, the MLP, specifically for image noise reduction. The results show as expected that the MLP performs worse than the CNN, also that the impact of the size of the dataset and choice of noise to be reduced is, albeit of great impact on the performance, not as important as the choice of neural network.
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