To address the serious noise pollution in the process of image remote sensing and the existence of object edge blur and artifacts in the super-resolution reconstructed image， this study proposes a remote sensing image super-resolution algorithm called edge-enhanced and non-local modules generative adversarial network （ENGAN）. To make the image edge details clearer， the proposed algorithm integrated an image edge enhancement module. To further expand the receptive field of the model and enhance the edge noise removal， the Mask branch in the edge enhancement module was simultaneously improved. The use of the intrinsic feature correlation of images further improved the reconstruction performance of the network. In this study， comparison experiments of multiple algorithms were performed on two remote sensing image datasets， UCAS-AOD and NWPU VHR-10. The proposed method showed improvement in multiple evaluation indicators. Taking degradation type IV as an example， the 4x super-resolution SSIM was increased by 0.068， PSNR increased by 1.400 dB， and RMSE reduced by 12.5% compared with the deep-blind super-resolution degradation model. Moreover， the reconstructed remote sensing image can obtain better ground target detection results than the original image.