Abstract: Deep learning-based image super-resolution algorithms often use recursive approaches or parameter sharing strategies to reduce network parameters， which increases the depth of the network and makes running the network time consuming， making it difficult to deploy the model to real-life situations. To solve the above problems， this paper designs a lightweight super-resolution network， which learns the correlation and importance of intermediate features， and combines the feature information of high-resolution images in the reconstruction part. First， the layer attention module is introduced to adaptively assign the weights of important hierarchical features by considering the correlation among layers. Next， finer feature information of the high-resolution image are extracted using an enhanced reconstruction block to obtain a clearer reconstructed image. A large number of comparative experiments show that the network designed in this paper has a smaller amount of network parameters compared with other lightweight models， and has a certain improvement in reconstruction accuracy and visual effects.

Key words: image super resolution, layer attention, intermediate features, enhanced reconstruction block, lightweight