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13 April 2009 The comparative analysis of image restoration represented as a matrix and as a vector using feed forward neural networks
Igor Mardare, Veacheslav Perju, David Casasent, Olga Ghincul
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Proceedings Volume 7340, Optical Pattern Recognition XX; 73400U (2009) https://doi.org/10.1117/12.819268
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States
Abstract
This work contains the results of the experiments on the restoration of the defective images proceeded in a matrix and a vector form with the help of the feed forward neural network. Sometimes it is convenient to represent an image as a vector rather than as a matrix. So the target of this work is to show experimentally what kind of input provides a better restoration, judging from the Euclid's distance of the output of a trained network. This work also shows the differences between processing different types of image presentation of the neuron network. Making a comparative analysis of a matrix and a vector form of presenting the images which are proceeded to a feed forward network allows stating some specific characteristics of a network. These characteristics include the optimal architecture of a network, the number of layers, the number of neurons in each layer and the time of an image restoration. Taking into account the network's characteristics and the most important factor - the Euclid's distance, are drawn conclusions that concern what is the best way of representing images that we want to restore using a feed forward network.
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Igor Mardare, Veacheslav Perju, David Casasent, and Olga Ghincul "The comparative analysis of image restoration represented as a matrix and as a vector using feed forward neural networks", Proc. SPIE 7340, Optical Pattern Recognition XX, 73400U (13 April 2009); https://doi.org/10.1117/12.819268
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KEYWORDS
Neural networks
Image quality
Image restoration
Neurons
Calibration
Image analysis
Image processing
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