In recent years,convolutional neural networks have been widely used in the field of image super-resolution.The super-resolution algorithm based on convolutional neural network has some problems,such as insufficient feature extraction of image,large number of parameters and difficult training.Therefore,this paper proposes a lightweight image super-resolution reconstruction algorithm based on gated convolutional neural network (GCNN).Firstly,the shallow feature extraction of the original low-resolution image is carried out by convolution operation.Then,the gated residual blocks (GRB) and long and short residual connections fully extract image features,and its high-efficient structure can also accelerate the network training process.The gated unit (GU) in the GRB uses the regional self-attention mechanism to extract the weight of each feature point in the input feature map.And then it multiplies the gate weight by the input feature element by element as the output of the GU.Finally,high-resolution images are reconstructed using sub-pixel convolution and convolution module.Experiments are conducted on Set14,BSD100,Urban100 and Manga109 datasets.Compared with the classical methods,not only does the algorithm in this paper have higher peak signal-to-noise ratio and structural similarity,but also the reconstructed image has clearer contour edges and details.