  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.
