  In recent years, deep convolutional neural networks have shown fascinating
performance in the field of image denoising. However, deeper network
architectures are often accompanied with large numbers of model parameters,
leading to high training cost and long inference time, which limits their
application in practical denoising tasks. In this paper, we propose a novel
dual convolutional blind denoising network with skip connection (DCBDNet),
which is able to achieve a desirable balance between the denoising effect and
network complexity. The proposed DCBDNet consists of a noise estimation network
and a dual convolutional neural network (CNN). The noise estimation network is
used to estimate the noise level map, which improves the flexibility of the
proposed model. The dual CNN contains two branches: a u-shaped sub-network is
designed for the upper branch, and the lower branch is composed of the dilated
convolution layers. Skip connections between layers are utilized in both the
upper and lower branches. The proposed DCBDNet was evaluated on several
synthetic and real-world image denoising benchmark datasets. Experimental
results have demonstrated that the proposed DCBDNet can effectively remove
gaussian noise in a wide range of levels, spatially variant noise and real
noise. With a simple model structure, our proposed DCBDNet still can obtain
competitive denoising performance compared to the state-of-the-art image
denoising models containing complex architectures. Namely, a favorable
trade-off between denoising performance and model complexity is achieved. Codes
are available at https://github.com/WenCongWu/DCBDNet.
