Light-weight Image Super-resolution Reconstruction Method of Spatial Channel Fusion
REN Zhongyi
HU Lihua
ZHANG Sulan
Abstract：Image super resolution reconstruction technology can improve image identification ability and accuracy,with the continuous development of deep learning,because of its excellent fitting ability,the generated adversarial network shows a good po-tential in the field of image super resolution reconstruction rate.The SRGAN algorithm network model is improved,a light-weight network model based on attention mechanism is proposed.The spatial channel attention mechanism is introduced to improve the fea-ture extraction ability of the generator module,so that the network can obtain the high frequency feature information of the image adaptively,and further improve the performance of the generated network.At the same time,the number of network layers of the dis-criminator module is compressed,and more efficient convolution method and activation function are adopted to accelerate the con-vergence of the model and improve the quality of the reconstructed image.The effectiveness of the improved method is verified by the test results of the public data set,the improved model parameters are greatly reduced,the texture details are restored more clear-ly,and the image visual effect is better.
Keywords：super-resolution reconstructiongenerative adversarial networksattention mechanismlight-weight
Publication Date：2025-11-20
Online Publishing Date：2026-01-28(First online date of this platform, not the publication date of the document)
Pages：8( 3220-3227 )