Classifying degraded images over various levels of degradation [article]

Kazuki Endo, Masayuki Tanaka, Masatoshi Okutomi
<span title="2020-06-15">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2006.08145v1">arXiv:2006.08145v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ov2hzjv7rfgn3czxdckaoodiqm">fatcat:ov2hzjv7rfgn3czxdckaoodiqm</a> </span>
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