The resolution of optical imaging is limited by the diffraction limit, system detector size and many other factors. To obtain images with richer details and clearer textures, a multi-scale feature attention fusion residual network was proposed. Firstly, shallow features of the image were extracted using a layer of convolution and then the multi-scale features were extracted by a cascade of multi-scale feature extraction units. The local channel attention module is introduced in the multi-scale feature extraction unit to adaptively correct the weights of feature channels and improve the attention to high frequency information. The shallow features and the output of each multi-scale feature extraction unit were used as hierarchical features for global feature fusion reconstruction. Finally, the hight-resolution image was reconstructed by introducing shallow features and multi-level image features using the residual branch. Charbonnier loss was adopted to make the training more stable and converge faster. Comparative experiments on the international benchmark datasets show that the model outperforms most state-of-the-art methods on objective metrics. Especially on the Set5 data set, the PSNR index of the 4× reconstruction result is increased by 0.39 dB, and the SSIM index is increased to 0.8992, and the subjective visual effect of the algorithm is better.