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Classifying degraded images over various levels of degradation
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<span title="2020-06-15">2020</span>
<i >
arXiv
</i>
<span class="release-stage" >pre-print</span>
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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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