Classification of massive noisy image using auto-encoders and convolutional neural network

Image processing tasks has found a new dimension with the improvement of learning feature representation from images using deep networks. Most of the research works are conducted over pre-possessed image data in the lab. But, these methods fail in the real world scenario as most of the time the imag...

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Vydané v:2017 8th International Conference on Information Technology (ICIT) s. 971 - 979
Hlavní autori: Roy, Sudipta Singha, Ahmed, Mahtab, Akhand, M. A. H.
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.05.2017
Predmet:
convolution
Convolutional Autoencoder
Convolutional Neural Network
Denoising Autoencoder
Image classification
Image denoising
Image reconstruction
Kernel
Neural networks
Noise measurement
Noise reduction
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Popis
Shrnutí:Image processing tasks has found a new dimension with the improvement of learning feature representation from images using deep networks. Most of the research works are conducted over pre-possessed image data in the lab. But, these methods fail in the real world scenario as most of the time the image required to classify is subject to noise and other disfigurement. For the last three decades, many researches has been conducted and numerus algorithms have been proposed with varying performances to classify noisy images. But in recent times, various autoencoders have outperformed all traditional methods for reconstructing native image from it's noisy form and opened a new door for the research of noisy image classification. In this paper, we studied various auto encoders for reconstructing native images from noisy input images. We have applied convolutional neural network as classifier. Before classification task we have rectified noisy images using denoising autoencoder, convolutional denoising autoencoder and finally a hybrid of them as proposed in this paper. The proposed methods are evaluated by experimenting over benchmark dataset adulterated with noises of different proportionate. This method has outperformed some other prominent methods achieving satisfying classification accuracy even when the image is too much noisy (50% noise is added with the image data).
DOI:10.1109/ICITECH.2017.8079976