Module Similarity-Based Pruning for Image Super-Resolution Network

This paper proposes a network pruning method for single image super-resolution network (SISR). This method evaluates the similarity of each module in the super-resolution network and uses a simple method to convert the similarity into the contribution degree of each module to the network, and find the relatively unimportant modules of the network to perform network pruning. Through the method of network pruning for the super-resolution network based on the module similarity, the super-resolution network with a huge amount of parameters is compressed, and the number of parameters and the amount of calculation are greatly reduced. Experiments show that the parameters of the super-resolution network after pruning can be reduced by more than 60%, while the accuracy is not reduced by more than 0.1%, which has great practical significance for the deployment of the super-resolution network to a low-performance platform.