  Image denoising is an important low-level computer vision task, which aims to
reconstruct a noise-free and high-quality image from a noisy image. With the
development of deep learning, convolutional neural network (CNN) has been
gradually applied and achieved great success in image denoising, image
compression, image enhancement, etc. Recently, Transformer has been a hot
technique, which is widely used to tackle computer vision tasks. However, few
Transformer-based methods have been proposed for low-level vision tasks. In
this paper, we proposed an image denoising network structure based on
Transformer, which is named DenSformer. DenSformer consists of three modules,
including a preprocessing module, a local-global feature extraction module, and
a reconstruction module. Specifically, the local-global feature extraction
module consists of several Sformer groups, each of which has several
ETransformer layers and a convolution layer, together with a residual
connection. These Sformer groups are densely skip-connected to fuse the feature
of different layers, and they jointly capture the local and global information
from the given noisy images. We conduct our model on comprehensive experiments.
Experimental results prove that our DenSformer achieves improvement compared to
some state-of-the-art methods, both for the synthetic noise data and real noise
data, in the objective and subjective evaluations.
