At present, the main super-resolution (SR) method based on convolutional neural network (CNN) is to increase the layer number of the network by skip connection so as to improve the nonlinear expression ability of the model. However, the network also becomes difficult to be trained and converge. In order to train a smaller but better performance SR model, this paper constructs a novel image SR network of multiple attention mechanism(MAMSR), which includes channel attention mechanism and spatial attention mechanism. By learning the relationship between the channels of the feature map and the relationship between the pixels in each position of the feature map, the network can enhance the ability of feature expression and make the reconstructed image more close to the real image. Experiments on public datasets show that our network surpasses some current state-of-the-art algorithms in PSNR, SSIM, and visual effects.

目前，基于卷积神经网络（CNN）的主要超分辨率（SR）方法是通过跳过连接来增加网络的层数，以提高模型的非线性表达能力。然而，网络也变得难以训练和融合。为了训练更小但性能更好的SR模型，本文构建了一种多关注机制（MAMSR）的新型图像SR网络，该网络包括渠道关注机制和空间关注机制。通过学习特征图的通道之间的关系以及特征图的每个位置中的像素之间的关系，网络可以增强特征表达的能力，并使重建图像更接近真实图像。