  Since convolutional neural networks perform well in learning generalizable
image priors from large-scale data, these models have been widely used in image
denoising tasks. However, the computational complexity increases dramatically
as well on complex model. In this paper, We propose a novel lightweight
Complementary Attention Module, which includes a density module and a sparse
module, which can cooperatively mine dense and sparse features for feature
complementary learning to build an efficient lightweight architecture.
Moreover, to reduce the loss of details caused by denoising, this paper
constructs a gradient-based structure-preserving branch. We utilize
gradient-based branches to obtain additional structural priors for denoising,
and make the model pay more attention to image geometric details through
gradient loss optimization.Based on the above, we propose an efficiently Unet
structured network with dual branch, the visual results show that can
effectively preserve the structural details of the original image, we evaluate
benchmarks including SIDD and DND, where SCANet achieves state-of-the-art
performance in PSNR and SSIM while significantly reducing computational cost.
