  In the past few decades, to reduce the risk of X-ray in computed tomography
(CT), low-dose CT image denoising has attracted extensive attention from
researchers, which has become an important research issue in the field of
medical images. In recent years, with the rapid development of deep learning
technology, many algorithms have emerged to apply convolutional neural networks
to this task, achieving promising results. However, there are still some
problems such as low denoising efficiency, over-smoothed result, etc. In this
paper, we propose the Edge enhancement based Densely connected Convolutional
Neural Network (EDCNN). In our network, we design an edge enhancement module
using the proposed novel trainable Sobel convolution. Based on this module, we
construct a model with dense connections to fuse the extracted edge information
and realize end-to-end image denoising. Besides, when training the model, we
introduce a compound loss that combines MSE loss and multi-scales perceptual
loss to solve the over-smoothed problem and attain a marked improvement in
image quality after denoising. Compared with the existing low-dose CT image
denoising algorithms, our proposed model has a better performance in preserving
details and suppressing noise.
