Objective To address the problems of a large number of parameters and insufficient image feature extraction ability in super-resolution networks which lead to poor quality of reconstructed images a new image super-resolution model LHNet is proposed. Methods In the deep feature extraction module a symmetric dense CNN was designed. The convolution module was connected in a symmetric structure to enhance the model?? s ability to extract local image information. A dual attention module was introduced to make the network focus on pixel and spatial information. In addition the efficient Transformer modules were connected using a dense connection method to strengthen the model?? s global feature extraction ability thus better restoring the image texture details. Results The experimental results were tested on five datasets namely Set5 Set14 BSD100 Urban100 and Manga109. The results showed that on the Set5 dataset when the magnification factor was 3 the LHNet method reduced the number of parameters by 99 K compared with the IMDN method. Meanwhile the PSNR value increased by 0. 13 dB and the SSIM increased by 0. 000 9. Compared with other current methods such as LAPAR-A and ShuffleMixer the LHNet method also showed superior performance. Conclusion The LHNet method can improve the quality of reconstructed images while using a relatively small number of parameters thereby achieving a balance between the number of parameters and performance.