  Noise removal of images is an essential preprocessing procedure for many
computer vision tasks. Currently, many denoising models based on deep neural
networks can perform well in removing the noise with known distributions (i.e.
the additive Gaussian white noise). However eliminating real noise is still a
very challenging task, since real-world noise often does not simply follow one
single type of distribution, and the noise may spatially vary. In this paper,
we present a new dual convolutional neural network (CNN) with attention for
image blind denoising, named as the DCANet. To the best of our knowledge, the
proposed DCANet is the first work that integrates both the dual CNN and
attention mechanism for image denoising. The DCANet is composed of a noise
estimation network, a spatial and channel attention module (SCAM), and a CNN
with a dual structure. The noise estimation network is utilized to estimate the
spatial distribution and the noise level in an image. The noisy image and its
estimated noise are combined as the input of the SCAM, and a dual CNN contains
two different branches is designed to learn the complementary features to
obtain the denoised image. The experimental results have verified that the
proposed DCANet can suppress both synthetic and real noise effectively. The
code of DCANet is available at https://github.com/WenCongWu/DCANet.
