In order to solve the problems of artifacts and unreal details when process low-resolution blurred images using image enhancement methods, a low-resolution blurred image enhancement algorithm based on deep dense residual generative adversarial network (DDR GAN) was used to achieve effective enhancement of low-quality images. Firstly, an end-to-end generative adversarial network framework was constructed; further, a deep dense residual latent feature encoding architecture was designed to improve the deep semantic feature representation of the input image and enhance the image generation efficiency; finally, the loss function was reconstructed and the perceptual loss was added to guide the model to learn to generate realistic images. The comparative experimental results show that compared with the current state-of-the-art enhanced super-resolution GAN(ESR GAN) and DeBlur GAN-V2 algorithms, the images generated by DDR GAN are visually better, with higher definition and richer image detail. In terms of objective evaluation indicators, compared with ESR GAN and DeBlur GAN-V2, the peak signal-to-noise ratio was improved by 1.7072 dB and 1.1683 dB through DDR GAN, respectively, and the structural similarity by 0.0783 and 0.0713, respectively. This algorithm is helpful for the restoration and enhancement of low-resolution blurred images.