Recently, convolutional neural networks (CNN) have been widely used in image denoising. But with most CNN denoising methods, all the channels are treated equally and the relationship between spatial locations are neglected. In the letter, we propose a novel channel and space attention neural network (CSANN) for image denoising. In CSANN, we concatenate the noise level with the average and maximum values of each channel as the input and propose a convolutional network to learn the relationship between channels. Meanwhile, we combine the noise level map with the average and maximum values of each spatial locations as the input and use a convolutional network to learn the relationship between spatial locations. Moreover, we combine them as an attention network and introduce it into the main CNN and symmetric skip connections, which makes channels related to attention network play different roles in the subsequent convolution and offsets the performance degradation caused by using a single convolution kernel in spatial locations. In addition, the use of symmetric skip connections and resnet blocks avoid the vanishing gradient problem and the loss of shallow features. Experimental results show that, compared with some state-of-the-art denoising algorithms, the experimental results of CSANN have better visual effects and higher peak signal-to-noise ratio (PSNR) values.

中文翻译：

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通道和空间注意神经网络的图像去噪

最近，卷积神经网络（CNN）已被广泛用于图像去噪。但是对于大多数的CNN去噪方法，所有通道均被平等对待，而空间位置之间的关系则被忽略了。在信中，我们提出了一种用于图像去噪的新型通道和空间注意神经网络（CSANN）。在CSANN中，我们将噪声水平与每个通道的平均值和最大值作为输入进行级联，并提出一个卷积网络以了解通道之间的关系。同时，我们将噪声水平图与每个空间位置的平均值和最大值作为输入进行组合，并使用卷积网络学习空间位置之间的关系。此外，我们将它们结合为关注网络，并将其引入到主要的CNN和对称跳过连接中，这使得与注意力网络相关的通道在随后的卷积中扮演不同的角色，并弥补了在空间位置使用单个卷积核导致的性能下降。此外，使用对称的跳过连接和resnet块可以避免梯度消失和浅层特征丢失的问题。实验结果表明，与某些最新的降噪算法相比，CSANN的实验结果具有更好的视觉效果和更高的峰值信噪比（PSNR）值。使用对称跳过连接和resnet块可以避免梯度消失和浅层特征丢失的问题。实验结果表明，与某些最新的降噪算法相比，CSANN的实验结果具有更好的视觉效果和更高的峰值信噪比（PSNR）值。使用对称跳过连接和resnet块可以避免梯度消失和浅层特征丢失的问题。实验结果表明，与某些最新的降噪算法相比，CSANN的实验结果具有更好的视觉效果和更高的峰值信噪比（PSNR）值。