Real World Degradation

Real-world degradation research focuses on improving the robustness of machine learning models, particularly in image and video processing, by addressing the discrepancies between controlled training data and the complexities of real-world scenarios. Current efforts concentrate on generating synthetic data that accurately mimics diverse degradations like blur, noise, and compression artifacts, often employing generative adversarial networks (GANs) and diffusion models to create realistic low-resolution images from high-resolution counterparts. This research is crucial for advancing applications such as face recognition, autonomous driving, and remaining useful life prediction, where reliable performance under diverse and unpredictable conditions is paramount.