  In this paper, we introduce a new CT image denoising method based on the
generative adversarial network (GAN) with Wasserstein distance and perceptual
similarity. The Wasserstein distance is a key concept of the optimal transform
theory, and promises to improve the performance of the GAN. The perceptual loss
compares the perceptual features of a denoised output against those of the
ground truth in an established feature space, while the GAN helps migrate the
data noise distribution from strong to weak. Therefore, our proposed method
transfers our knowledge of visual perception to the image denoising task, is
capable of not only reducing the image noise level but also keeping the
critical information at the same time. Promising results have been obtained in
our experiments with clinical CT images.
