  Deep convolutional neural networks (CNNs) for image denoising have recently
attracted increasing research interest. However, plain networks cannot recover
fine details for a complex task, such as real noisy images. In this paper, we
propsoed a Dual denoising Network (DudeNet) to recover a clean image.
Specifically, DudeNet consists of four modules: a feature extraction block, an
enhancement block, a compression block, and a reconstruction block. The feature
extraction block with a sparse machanism extracts global and local features via
two sub-networks. The enhancement block gathers and fuses the global and local
features to provide complementary information for the latter network. The
compression block refines the extracted information and compresses the network.
Finally, the reconstruction block is utilized to reconstruct a denoised image.
The DudeNet has the following advantages: (1) The dual networks with a parse
mechanism can extract complementary features to enhance the generalized ability
of denoiser. (2) Fusing global and local features can extract salient features
to recover fine details for complex noisy images. (3) A Small-size filter is
used to reduce the complexity of denoiser. Extensive experiments demonstrate
the superiority of DudeNet over existing current state-of-the-art denoising
methods.
