Low quality images become more challenge and core problem in recent decade because of the ambiguity of contents of them. Convolutional deep neural networks are used for solving this problem. In this work, we used a combination of convolutional neural network and deep belief network to construct an efficient model able to classify low quality images. This model has the capability in extracting effective features from low quality images. Data augmentation is used through this model to increase the accuracy of the system. Scikit-Learn python library is used in implementation the system on STL-10 dataset. The results showed that the proposed model increase the accuracy of the system by 0.20%.
El-Ashmony, E., El-Dosuky, M., & Elmougy, S. (2016). CLASSIFICATION OF LOW QUALITY IMAGES USING CONVOLUTIONAL NEURAL NETWORK AND DEEP BELIEF NETWORK. International Journal of Intelligent Computing and Information Sciences, 16(4), 19-28. doi: 10.21608/ijicis.2016.19822
MLA
E. El-Ashmony; M. El-Dosuky; Samir Elmougy. "CLASSIFICATION OF LOW QUALITY IMAGES USING CONVOLUTIONAL NEURAL NETWORK AND DEEP BELIEF NETWORK". International Journal of Intelligent Computing and Information Sciences, 16, 4, 2016, 19-28. doi: 10.21608/ijicis.2016.19822
HARVARD
El-Ashmony, E., El-Dosuky, M., Elmougy, S. (2016). 'CLASSIFICATION OF LOW QUALITY IMAGES USING CONVOLUTIONAL NEURAL NETWORK AND DEEP BELIEF NETWORK', International Journal of Intelligent Computing and Information Sciences, 16(4), pp. 19-28. doi: 10.21608/ijicis.2016.19822
VANCOUVER
El-Ashmony, E., El-Dosuky, M., Elmougy, S. CLASSIFICATION OF LOW QUALITY IMAGES USING CONVOLUTIONAL NEURAL NETWORK AND DEEP BELIEF NETWORK. International Journal of Intelligent Computing and Information Sciences, 2016; 16(4): 19-28. doi: 10.21608/ijicis.2016.19822
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