Image Restoration with Operators Modeled by Artificial Neural Networks

Ana Paula Abrantes de Castro, José Demisio Simões da Silva, Elcio Hideiti Shiguemori
2009 2009 XXII Brazilian Symposium on Computer Graphics and Image Processing  
This paper presents a new approach to image restoration based on ANN, considering the learning of the inverse process using a standard image for training under a multiscale approach. Dierent models of ANN were tested and compared with the traditional techniques. The standard image was articially degraded to simulate some types of frequent degradation problems. Due to the huge amount of data generated for training the ANN, this paper uses clustering techniques to reduce the training set. The
more » ... r proposes a simple restoration method that leads to a sub-optimal solution without the need of prior knowledge estimation of the degradation phenomenon. The ANN based lters were tested with dierent kinds of degraded images. The mean squared error and the signal-to-noise ratio were used as performance indices to measure the quality of the results of the ANN and of some of the existing methods for comparison. The results show that the ANN based restoration algorithms as proposed in this paper are eective restoration methods. The main advantage of the proposed approach is related to the fact that it does not require an estimation of prior knowledge of the degradation causes for each image.
doi:10.1109/sibgrapi.2009.44 dblp:conf/sibgrapi/CastroSS09 fatcat:lcgzaedinvf7rlsspyldsppzyi