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arXiv · 2006.08145

Classifying degraded images over various levels of degradation

Kazuki Endo·Masayuki Tanaka·Masatoshi Okutomi
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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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Kazuki Endo, Masayuki Tanaka, Masatoshi Okutomi. 2020-06-15. Classifying degraded images over various levels of degradation. https://arxiv.org/abs/2006.08145

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