Multi-Scale Residual Fusion Network for Super-Resolution Reconstruction of Single Image

Abstract

Inferring all missing high-frequency details by using low-resolution image is the key to super-resolution reconstruction of a single image. In order to extract the feature information in low-resolution images fully and maximize the deduction of high-frequency details, we introduce a multi-scale cross merge (MSCM) network based on residual fusion. The MSCM uses feature extraction module with different-sized convolutional kernels to extract multiple features from the low resolution input image and send them into a nonlinear mapping module after concatenate them together. The nonlinear mapping module consists of five cross-merge modules, each of them formed by cascading three residual dual-branch merged structures. This structure can promote information integration of different branches. Dense connection and residual connection are integrated into the nonlinear mapping module to improve the transmission of information and gradient. The nonlinear mapping module is responsible for extracting high-frequency features and sending them to reconstruction module, which combines an improved sub-pixel up-sampling layer with external residual and global residual to generate a high resolution image. Simulation experiments demonstrate that our MSCM network has the ability of achieving single-image super-resolution reconstruction, and offers objective and subjective quality improvement compared to mainstream methods and other state-of-the-art reconstruction methods.