Image Super-Resolution based on Multi-Convolution Neural Network

In recent years, convolutional neural network method has been widely and successfully applied in the field of image super-resolution. With the development of CNN structure, the reconstruction algorithm based on CNN has also been developed. However, in these reconstruction models, the scale of convolution operator is single. This will greatly limit the model's learning ability about input image and impact the reconstruction effect. In order to improve the accuracy of the convolution network to the input image, a reconstruction method based on multi-scale convolution operator is proposed. In this method, multi-scale convolution operators are set up in each layer network to calculate multi-scale features of the input image. Experiments show that this method can effectively improve the accuracy of the reconstructed image in detail.