Multi-directional feature fusion super-resolution network based on nonlinear spiking neural P systems

Abstract

Single-image super-resolution (SISR) aims to recover lost details and textures from limited information to improve the visual quality and detail clarity of an image. Most of the existing convolutional neural network (CNN)-based SISR methods do not make full use of the intermediate features of the network, and at the same time its processing process can result in the loss of information details. To address these problems, this paper recommends a novel multi-directional feature fusion super-resolution network. The direction-aware mechanism with nonlinear spiking neural P (NSNP) systems is integrated to design a multidirectional feature extraction module, while different NSNP-like convolution modules are constructed for extracting deep features on the basis of nonlinear spiking mechanism. Moreover, in the residual feature enhancement module, three different branches are designed using these NSNP-like convolution modules for feature enhancement and fusion to decrease the loss of information. The preposed network is evaluated on five benchmark datasets, and the experimental results show that the network performs a better reconstruction with good capability of generalization compared to most state-of-the-art methods.