Image Super-Resolution Reconstruction Based on Attention Mechanism and Feature Fusion

Abstract

In super-resolution image reconstruction,most of the existing methods using Convolutional Neural Network(CNN) neglect the inherent attributes of natural images,and extract features only at a single scale.To address the problem,this paper proposes a network structure based on attention mechanism and multi-scale feature fusion.By using the attention mechanism,the non-local information and second-order features of the image are fused to improve the feature expression ability of the network.At the same time,different scales of convolutional kernels are used to extract different scales of information of the image,so as to preserve the complete information characteristics at different scales.Experimental results show that the reconstructed image by the proposed method outperforms Bicubic,SRCNN,SCN and LapSRN methods in terms of objective evaluation metrics and visual quality.