Abstract
A high-order feedforward neural architecture, called pi t -sigma (π t σ) neural network, is proposed for lossy digital image compression and reconstruction problems. The π t σ network architecture is composed of an input layer, a single hidden layer, and an output layer. The hidden layer is composed of classical additive neurons, whereas the output layer is composed of translated multiplicative neurons (π t -neurons). A two-stage learning algorithm is proposed to adjust the parameters of the π t σ network: first, a genetic algorithm (GA) is used to avoid premature convergence to poor local minima; in the second stage, a conjugate gradient method is used to fine-tune the solution found by GA. Experiments using the Standard Image Database and infrared satellite images show that the proposed π t σ network performs better than classical multilayer perceptron, improving the reconstruction precision (measured by the mean squared error) in about 56%, on average.
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Iyoda, E.M., Shibata, T., Nobuhara, H. et al. Image Compression and Reconstruction Using pi t -Sigma Neural Networks. Soft Comput 11, 53–61 (2007). https://doi.org/10.1007/s00500-006-0052-z
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DOI: https://doi.org/10.1007/s00500-006-0052-z