Classifying degraded images over various levels of degradation
This work addresses the practical challenge of image classification under degradation, but it appears incremental as it builds on existing methods like CNNs and ensemble learning without introducing a major breakthrough.
The paper tackled the problem of classifying images with varying levels of degradation by proposing a convolutional neural network that combines a restoration network and ensemble learning, achieving good classification performance as demonstrated in the results.
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.