BSRGAN addresses the challenging task of super-resolving real-world images with unknown degradation by learning a practical degradation model. The method trains a deep network (BSRNet) to generate realistic low-resolution inputs from high-quality images, enabling the model to handle diverse real-world blur, noise, and downscaling artifacts. Published at ICCV 2021, the repository includes both inference and training code in PyTorch.