The traditional CNN-based method can not distinguish between low-frequency structural information and high-frequency detailed information in the process of reconstructing low-resolution images. And there is a lack of information communication between layers of the network, which leads to the problem of missing information in the high-resolution reconstruction image. In order to obtain more information about the structure and details of each level of image features a residual dense network is constructed based on the wavelet domain (WRDSR). In the wavelet domain formed by the two-dimensional discrete wavelet transform, the network uses dense connections and residual connections to fully extract the information of different frequencies of the image, and then generates high-resolution image wavelets by inputting the fused features into sub-pixel convolution. Finally, the final high-resolution image is generated by two-dimensional discrete wavelet inversion. Compared with Bicubic, A+, SRCNN, VDSR, LapSRN, DWSR,SDSR etc., WRDSR improves 2.824 dB/0.059 5, 0.896 dB/0.018 2, 0.747 dB/0.016 8, 0.016 dB/0.002 4, 0.025 dB/0.004 3, 0.21 dB/0.004 7 and 0.20 dB/0.0057 on average on PSNR/SSIM, respectively. While making more efficient use of the original image information, WRDSR solves the drawback of missing information, making the reconstructed image texture clearer, richer in details and better in visual effect.