The overexposure of laser image leads to poor imaging quality. A self-repairing method of overexposure of laser image under visual communication technology based on full-scale feature aggregation is proposed under visual communication. The overexposure feature points of laser image are detected by the deep fusion segmentation method of spectrum and texture features, and the differences of feature information are analyzed. The full-scale feature extraction of laser image is realized by the dynamic fusion method of visual communication combined with the changing regional distribution of pixels. The deep learning model is used to realize the visual aggregation of features, and the weights are allocated in the channel and spatial dimensions according to the differential characteristics of full-scale feature distribution. The self-healing of laser images is realized according to the view sensing and visual communication on multiple scales. Simulation tests show that compared with the other two methods, the PSNR value, SSIM value and FSIM value of the proposed method are higher, which are 38.56, 0.97 and 0.97 respectively, indicating that the overall quality, structure and details of the image repaired by the proposed method and the image feature retention are better. It shows that this method can reconstruct fine laser image by overexposure repair process, and effectively solve the problem of spatial information dispersion and spatial grid distribution imbalance caused by overexposure.