Aiming at the problems of the image super-resolution reconstruction (SR) model based on convolutional neural network (CNN), such as insufficient feature extraction, a large number of parameters caused by too deep network, and the impact of redundant information on the final reconstruction performance of the network, this paper designs a lightweight densely connected image super-resolution network (LDCN). The network designs a multi-scale iterative feature extraction module (MIFEM), to achieve full extraction of multi-scale features in the case of lower parameters; according to the idea of residual shrinkage, a key information extraction module (KIEM) is constructed, which can remove more redundant information than the original module, so that the network can fully pay attention to the key information and the overall parameters of the module are reduced by 72%; finally, the feature transfer module (FTM) is introduced into the dense residual network, which further reduces the complexity of the model and solves the problem of deep model layers and large parameters. Experimental results show that LDCN outperforms mainstream models in both reconstruction performances and visual perceptions. On the four test sets, compared with the lightweight model MADNet, the PSNR is increased by 0.1 dB,0.11 dB,0.06 dB, and 0.26 dB, respectively, and the number of parameters is only 47.6% of MADNet.