Image super-resolution via multi-path recursive convolutional network

Convolutional neural network (CNN) has recently achieved a great success for single image super-resolution (SISR). However, most deep CNN-based super-resolution models use chained stacking to build the network, which results in the fact that the relationship between layers is weak and does not make full use of hierarchical features. In this paper, a multi-path recursive convolutional network (MRCN) is designed to address these problems in SISR. By using multi-path structure to strengthen the relationship between layers, our network can ef-fectively utilize features and extract rich high-frequency components. At the same time, we also use recursive structure to alleviate training difficulty. In addition, by introducing the operation of feature fusion into the model, our network can make full use of the features extracted from each layer in the reconstruction process and select the effective features adaptively. Extensive experiments on benchmarks datasets have shown that MRCN has a significant performance improvement against existing methods.