Abstract: Aiming at the problems of weak feature extraction ability and complex model parameters in existing image super-resolution models,an image super-resolution reconstruction model based on structured hybrid attention network is proposed.This model can reduce the number of parameter while improving the super-resolution reconstruction effect.First,the encoder is structured to extract more image features through the difference in the number of channels.Second,the attention network hybrid reorganization is performed on the output features of the encoder to enhance the feature characteristics of the image.Finally,a residual method is used to directly mix the input shallow image features with the enhanced features to reduce the amount of network parameters.Experimental results show that under the premise of public data sets and different magnifications,the PSNR value and SSIM value of the proposed model are basically optimal,and the parameter amount of the network structure is low,which better balances the relationship between performance and parameter complexity in the process of image super-resolution reconstruction.

Key words: Image processing, Super-resolution, Structured residuals, Fused attention, Low model parameters