A lightweight Laplacian pyramid image super-resolution reconstruction convolution neural network based on deep Laplacian pyramid networks （LapSRNs） is proposed to accommodate the numerous parameters used in super-resolution reconstruction methods based on deep learning. First， shallow features are embedded from the input low resolution image （LR） input. Subsequently， using recursive blocks that allow parameter sharing and contain shared-source skip connections， deep features are extracted from the shallow features. Additionally， residual image （RI） containing high-frequency information is inferred. Next， the RI and input LR are upsampled via a transposed convolutional layer and added pixel by pixel to obtain a super-resolution image. The total number of parameters used in this method is only 3.98% of that used in the LapSRN for three scales， and the peak signal to noise ratio index increases by 0.031 3 and 0.116 7 dB under 4 times and 8 times super-resolutions， respectively. The proposed method reduces the number of parameters by 81.6%， 90.8%， and 88.8% under 2 times， 4 times， and 8 times resolutions， while the super-resolution effect is maintained.