NOISY IMAGE CLASSIFICATION USING HYBRID DEEP LEARNING METHODS
In real-world scenario, image classification models degrade in performance as the images are corrupted with noise, while these models are trained with preprocessed data. Although deep neural networks (DNNs) are found efficient for image classification due to their deep layer-wise design to emulate l...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
UUM Press
2018-02-01
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| Series: | Journal of ICT |
| Online Access: | https://www.scienceopen.com/document?vid=5abac3da-cdfa-4845-a1ae-624dda649954 |