Classifying degraded images over various levels of degradation

• URL: http://arxiv.org/abs/2006.08145v1
• Date: Mon, 15 Jun 2020 05:43:07 GMT
• Title: Classifying degraded images over various levels of degradation
• Authors: Kazuki Endo, Masayuki Tanaka, Masatoshi Okutomi
• Abstract summary: This paper proposes a convolutional neural network to classify degraded images by using a restoration network and an ensemble learning. The results demonstrate that the proposed network can classify degraded images over various levels of degradation well.
• Score: 14.9119546783196
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Classification for degraded images having various levels of degradation is very important in practical applications. This paper proposes a convolutional neural network to classify degraded images by using a restoration network and an ensemble learning. The results demonstrate that the proposed network can classify degraded images over various levels of degradation well. This paper also reveals how the image-quality of training data for a classification network affects the classification performance of degraded images.

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