  For many computer vision problems, the deep neural networks are trained and
validated based on the assumption that the input images are pristine (i.e.,
artifact-free). However, digital images are subject to a wide range of
distortions in real application scenarios, while the practical issues regarding
image quality in high level visual information understanding have been largely
ignored. In this paper, in view of the fact that most widely deployed deep
learning models are susceptible to various image distortions, the distorted
images are involved for data augmentation in the deep neural network training
process to learn a reliable model for practical applications. In particular, an
image quality assessment based label smoothing method, which aims at
regularizing the label distribution of training images, is further proposed to
tune the objective functions in learning the neural network. Experimental
results show that the proposed method is effective in dealing with both low and
high quality images in the typical image classification task.
