Adverse Condition Image

Adverse condition image processing focuses on improving the performance of computer vision models when faced with degraded image quality due to factors like fog, rain, or low light. Current research heavily emphasizes unsupervised domain adaptation techniques, employing generative adversarial networks (GANs) and self-training methods to adapt models trained on clear images to adverse conditions, often incorporating novel loss functions or feature refinement strategies. These advancements are crucial for deploying robust computer vision systems in real-world scenarios where ideal imaging conditions are not guaranteed, impacting applications ranging from autonomous driving to remote sensing.