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.

I. Introduction

Single image Super-resolution (SISR) is to restore a low resolution image (LR) to a high resolution image (HR). This problem has always been an active research topic in the field of computer vision, and it is also a classic ill-posed problem of mapping from low-dimensional data to high-level data. At present, super-resolution reconstruction methods based on single image can be divided into three categories: interpolation based method, prior knowledge based method and depth learning based method. The methods based on interpolation include nearest neighbor, bilinear and bicubic interpolation. These methods are simple and efficient, but the quality of image reconstruction is limited. The method based on prior knowledge is based on the cognition of the researcher to the image features. The feature calculation way is designed based on the image prior information and the calculation method of reconstruction parameters is determined by the optimized customized model. Finally the image reconstruction is completed. Using this method can get better reconstruction results, but it also takes a long time to optimize the model. Although the convolution neural network (CNN) method and the method based on prior knowledge can improve the efficiency to a certain extent, these methods will still be affected by the calculation efficiency. The method based on deep learning can automatically learn the mapping relationship between low resolution image and high resolution image to complete the reconstruction work. In these methods, the prior knowledge used to guide image reconstruction does not need to be customized manually, but acquired through learning.