  Learning-based methods especially with convolutional neural networks (CNN)
are continuously showing superior performance in computer vision applications,
ranging from image classification to restoration. For image classification,
most existing works focus on very clean images such as images in Caltech-256
and ImageNet datasets. However, in most realistic scenarios, the acquired
images may suffer from degradation. One important and interesting problem is to
combine image classification and restoration tasks to improve the performance
of CNN-based classification networks on degraded images. In this report, we
explore the influence of degradation types and levels on four widely-used
classification networks, and the use of a restoration network to eliminate the
degradation's influence. We also propose a novel method leveraging a fidelity
map to calibrate the image features obtained by pre-trained classification
networks. We empirically demonstrate that our proposed method consistently
outperforms the pre-trained networks under all degradation levels and types
with additive white Gaussian noise (AWGN), and it even outperforms the
re-trained networks for degraded images under low degradation levels. We also
show that the proposed method is a model-agnostic approach that benefits
different classification networks. Our results reveal that the proposed method
is a promising solution to mitigate the effect caused by image degradation.
