Cascade residual-optimized image super-resolution reconstruction in Transformer network
In order to expand the adaptive learning ability of convolutional neural network in image super-resolution algorithm on multiple scale features and improve the network performance,this paper proposed an optimization structure of Transformer network based on cascade residual method for image super-resolu-tion reconstruction. Firstly,the network adopted a cascaded residual structure,which enhanced the itera-tive reuse and information sharing ability of low and middle order features;Secondly,channel attention mechanism was introduced into Transformer structure to enhance network feature expression and adaptive learning capability of channel weights;Finally,the sensing module in Transformer network structure was optimized as a cascade sensing module to expand the network depth and enhance the feature expression ca-pability of the model. Reconstruction tests of 2x,3x and 4x magnification were carried out on Set5,Set14,BSD100,Urban100 and Manga109 data sets and compared with mainstream methods. Objective evaluation results showed that under Set5 data set with 4x magnification factor,Compared with other mainstream methods,the peak signal-to-noise ratio of the image obtained in this paper is increased by 1.14 dB on average,and the average structural similarity is increased by 0.019. Combined with the sub-jective evaluation results,it is shown that the proposed method has better image reconstruction effect than other mainstream methods,and the restored image texture details are clearer.
万方数据
维普
