In image denoising, deep convolutional neural networks (CNNs) can obtain
favorable performance on removing spatially invariant noise. However, many of
these networks cannot perform well on removing the real noise (i.e. spatially
variant noise) generated during image acquisition or transmission, which
severely sets back their application in practical image denoising tasks.
Instead of continuously increasing the network depth, many researchers have
revealed that expanding the width of networks can also be a useful way to
improve model performance. It also has been verified that feature filtering can
promote the learning ability of the models. Therefore, in this paper, we
propose a novel Dual-branch Residual Attention Network (DRANet) for image
denoising, which has both the merits of a wide model architecture and
attention-guided feature learning. The proposed DRANet includes two different
parallel branches, which can capture complementary features to enhance the
learning ability of the model. We designed a new residual attention block (RAB)
and a novel hybrid dilated residual attention block (HDRAB) for the upper and
the lower branches, respectively. The RAB and HDRAB can capture rich local
features through multiple skip connections between different convolutional
layers, and the unimportant features are dropped by the residual attention
modules. Meanwhile, the long skip connections in each branch, and the global
feature fusion between the two parallel branches can capture the global
features as well. Moreover, the proposed DRANet uses downsampling operations
and dilated convolutions to increase the size of the receptive field, which can
enable DRANet to capture more image context information. Extensive experiments
demonstrate that compared with other state-of-the-art denoising methods, our
DRANet can produce competitive denoising performance both on synthetic and
real-world noise removal.

本文提出了一种基于 Dual-branch Residual Attention Network（DRANet）的图像降噪方法，通过增宽网络结构和注意力机制获得更好的去噪效果。实验证明，相比于其他已有的方法，该方法能够更好地处理合成和真实场景下的噪声。