双通道扩张卷积注意力图像去噪网络*

针对深度学习图像去噪算法存在网络过深导致细节丢失的问题，提出一种双通道扩张卷积注意力网络CEANet。拼接信息保留模块将每一层的输出特征图融合，弥补卷积过程中丢失的图像细节特征进行密集学习；扩张卷积可以在去噪性能和效率之间进行权衡，用更少的参数获取更多的信息，增强模型对噪声图像的表示能力，基于扩张卷积的稀疏模块通过扩大感受野获得重要的结构信息和边缘特征，恢复复杂噪声图像的细节；基于注意力机制的特征增强模块通过全局特征和局部特征进行融合，进一步指导网络去噪。实验结果表明，在高斯白噪声等级为25和50时，CEANet都获得了较高的峰值信噪比均值和结构相似性均值，能够更高效地捕获图像细节信息，在边缘保持和噪声抑制方面，具有较好的性能。相关实验证明了该算法进行图像去噪的有效性。

Two-channel dilated convolution attentional image denoising network

For image denoising, this paper proposed CEANet which had a dual-channel dilated convolution with attention mechanism to solve the problem of information loss caused by deep neural network. Reserving block merged output feature maps of each layer to make up the loss of detail information during convolution. Dilated convolution achieved better balance between denoising performance and efficiency, extracting more features with less parameters and enhancing the representation capability of the model for noisy images. The sparse module of dilated convolution expanded the receptive field to extract significant structural information and edge features and recover details of complicated noisy images. The feature enhancement module based on attention mechanism further guided network for image denoising by fusing global features with local features. The experimental results show that CEANet achieves high peak signal-to-noise ratio and structure similarity mean value at Gaussian white noise level of 25 and 50, which can capture image detail information more efficiently and has better performance in edge retention and noise suppression. Through the above comparative experiments, the effectiveness of the algorithm framework is proved.