提出了一种基于混合注意力机制和对偶残差学习的图像去噪网络。该网络采用了基于不同尺寸卷积核的对偶残差网络学习结构,不仅能降低更深网络结构的拟合难度,同时还能表示图像中的多尺度结构。所提去噪网络采用局部和非局部的混合注意力模块,对卷积神经网络的特征通道进行自适应调整,使得卷积神经网络不仅能注意图像的局部特征,还能刻画图像中的长距离依赖关系。与几种常见深度去噪网络的对比实验表明,本文算法能有效抑制不同强度的噪声,并且针对高强度噪声的去除性能更优。

In this paper, an image denoising network based on a hybrid attention mechanism and dual residual learning is proposed. The network uses a dual residual network learning structure based on different sizes of convolution kernels, which can not only reduce the difficulty of fitting deeper network structures, but also represent the multi-scale structure in the image. In the proposed denoising network, the feature channels are adaptively adjusted through hybrid local and non-local attention modules. Such hybrid attention module ensures that convolutional neural network can not only pay attention to the local features, but also depict the long-range dependencies in image. By comparing with several common deep denoising networks, the experimental results show that the proposed method can effectively suppress noise at different levels, specifically for the high-level noise.