The Probabilistic Degradation Model (PDM) is a novel approach that addresses the issue of blind image super-resolution without relying on known degradation models. PDM decomposes the image degradation process into independent components of blur kernel and noise, and uses generative models to characterize their distributions, thereby enhancing performance. This innovative method holds significant promise for making important strides in the field of computer vision, offering new possibilities. Additionally, PDM can serve as a data generator, integrating with existing super-resolution models to improve their performance in practical applications. This approach is expected to enhance image super-resolution, especially when faced with uncertain degradation models.