<p>Blind image deblurring is a crucial image restoration task aimed at estimating unknown blurring kernels from blurred images and then recovering potentially clear images. Conventional blind deblurring algorithms for recovering potentially clear images are often not noise-resistant. However, in reality, the noise in the input blurred image cannot be ignored, and Gaussian noise isnoise-resistant particularly common. To address the problems of image quality degradation and loss of detail information caused by Gaussian noise, we propose a hybrid operator edge detection algorithm for accurately identifying and distinguishing edge pixels and noise pixels of blurred images. Based on the local features of the texture edges of blurred images, our method can accurately extract the texture edges of images and apply the estimated edge matrix to the blind deblurring algorithm. Our method not only accurately recovers the latent image, but also significantly improves the performance and robustness. In this paper, the principle and process of the hybrid operator edge detection algorithm, and the improved blind image deblurring algorithm are described in detail. Through a large number of experiments, it is proved that the blind image deblurring algorithm incorporating the hybrid operator edge detection algorithm has good results under different types of images.</p>