关键词: 卷积神经网络, 超分辨率, 特征提取, 全局残差学习

Abstract:

Since the application of convolutional neural networks to the field of image super-resolution, more and more neural networks have been proposed and achieved good results. However, most current methods rely heavily on the depth and width of the model without fully utilizing the underlying information. Aiming at the above problems, a new feature enrichment network is proposed, which can gradually extract effective feature information through multiple feature concentration blocks. The network includes a feature extraction module, a feature concentration module and a reconstruction module, and bicubic interpolation operations and global residual learning are added. Firstly, useful features are extracted through the underlying feature processing, then feature concentration blocks are used to further extract features, finally a high-resolution image is reconstructed through a reconstruction module. In the experiment, 4 different public datasets are selected for testing at different scales. It can be seen from the experimental results that the proposed network has better objective index results than other methods.

Key words: convolutional neural network, super-resolution, feature extraction, global residual learning