论文标题
分类在各种降解级别上的降级图像
Classifying degraded images over various levels of degradation
论文作者
论文摘要
在实际应用中，具有不同级别降解的降级图像的分类非常重要。本文提出了一个卷积神经网络，以使用恢复网络和集合学习对降级图像进行分类。结果表明，所提出的网络可以很好地对各个级别的降解级别进行降解的图像进行分类。本文还揭示了分类网络的培训数据的图像质量如何影响降级图像的分类性能。
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.
