  Image blur and image noise are common distortions during image acquisition.
In this paper, we systematically study the effect of image distortions on the
deep neural network (DNN) image classifiers. First, we examine the DNN
classifier performance under four types of distortions. Second, we propose two
approaches to alleviate the effect of image distortion: re-training and
fine-tuning with noisy images. Our results suggest that, under certain
conditions, fine-tuning with noisy images can alleviate much effect due to
distorted inputs, and is more practical than re-training.
