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Title |
Multi-type Image Noise Classification by Using Deep Learning |
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Author |
Waqar Ahmed, Zahid Hussain Khand, Sajid Khan, Ghulam Mujtaba, Muhammad Asif Khan, Ahmad Waqas |
| Citation |
Vol. 24 No. 7 pp. 143-147 |
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Abstract |
Image noise classification is a classical problem in the field of image processing, machine learning, deep learning and computer vision. In this paper, image noise classification is performed using deep learning. Keras deep learning library of TensorFlow is used for this purpose. 6900 images images are selected from the Kaggle database for the classification purpose. Dataset for labeled noisy images of multiple type was generated with the help of Matlab from a dataset of non-noisy images. Labeled dataset comprised of Salt & Pepper, Gaussian and Sinusoidal noise. Different training and tests sets were partitioned to train and test the model for image classification. In deep neural networks CNN (Convolutional Neural Network) is used due to its in-depth and hidden patterns and features learning in the images to be classified. This deep learning of features and patterns in images make CNN outperform the other classical methods in many classification problems. |
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Keywords |
Convolutional Neural Networks, Keras, Deep Learning, Image Noise Classification, Machine Learning. |
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