Image Super-Resolution Reconstruction Based on Multi-Scale Residual Aggregation Feature Network

Aiming at the problems of single image feature extraction scale and insufficient utilization of middle level features in the existing image super-resolution reconstruction technology based on depth convolution neural network model, a multi-scale residual aggregation feature network model for image super-resolution reconstruction is proposed. First, the proposed network model uses expanded convolutions with different expanded coefficients and residual connection to construct a hybrid expanded convolution residual block (HERB), which can effectively extract multi-scale feature information of an image. Second, a feature aggregation mechanism (AM) is used to solve the problem of insufficient utilization of features among middle levels of the network. Experiments results on five commonly used data sets show that the proposed network model has better performance than other models in subjective visual effect and objective evaluation index.