The aim of this study was to investigate the DNN-based better noisy image classification system. At first, the autoencoder (AE)-based denoising techniques were considered to reconstruct native image from the input noisy image. | Journal of ICT, 17, No. 2 (April) 2018, pp: 233–269 How to cite this paper: Roy, S. S., Ahmed, M., & Akhand, M. A. H. (2018). Noisy image classification using hybrid deep learning methods. Journal of Information and Communication Technology, 17 (2), 233–269. NOISY IMAGE CLASSIFICATION USING HYBRID DEEP LEARNING METHODS Sudipta Singha Roy, 2Mahtab Ahmed & 2 Muhammad Aminul Haque Akhand 1 Institute of Information and Communication Technology Khulna University of Engineering & Technology, Khulna, Bangladesh 1 2 Dept. of Computer Science and Engineering Khulna University of Engineering & Technology, Khulna, Bangladesh ; mahtab@; akhand@ ABSTRACT 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 latent features from data, they suffer from the same noise issue. Noise in image is common phenomena in real life scenarios and a number of studies have been conducted in the previous couple of decades with the intention to overcome the effect of noise in the image data. The aim of this study was to investigate the DNN-based better noisy image classification system. At first, the autoencoder (AE)-based denoising techniques were considered to reconstruct native image from the input noisy image. Then, convolutional neural network (CNN) is employed to classify the reconstructed image; as CNN was a prominent DNN method with the ability to preserve better representation of the internal structure of the image data. In the denoising step, a variety of existing AEs, named denoising autoencoder (DAE), convolutional denoising autoencoder (CDAE) and denoising variational autoencoder Received: 17 October 2017 Accepted: 10 February 2018 233 Journal of ICT, 17, No. 2 (April) .
