Processing of noisy digital images with use of evolving autoencoders
DOI:
https://doi.org/10.15587/1729-4061.2017.116134Keywords:
digital image processing, noise filtering, evolution, population, artificial neural network, genetic algorithm, autoencoderAbstract
A promising class of neural network models used recently to solve the problems of recognition of noisy images are denoising autoencoders. In particular, the evolutionary approach can be effectively used in DAE to determine the network architecture, weights and learning algorithm. The proposed neural network evolving autoencoder allows efficient processing of noisy images due to the iterative learning procedure even in the presence of local distortions. When using the EDAE for determining the network architecture, weights and learning algorithm, standard evolutionary procedures (population initialization, population assessment, selection, crossover, mutation), as well as the evolutionary algorithm for the ANN adjustment and special chromosome formats are used.
The proposed approach to filtering and recognition of noisy images based on the EDAE application is promising for environmental monitoring of landscape and industrial areasReferences
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Copyright (c) 2017 Oleksandr Bezsonov, Oleg Rudenko, Serhii Udovenko, Olga Dudinova
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