Image Variation

Image variation research focuses on understanding and mitigating the impact of differing visual characteristics on image processing tasks. Current efforts concentrate on developing robust algorithms, such as those incorporating divisive normalization or leveraging diffusion models, to handle variations arising from environmental factors, differing image sources (synthetic vs. real), and even adversarial attacks. This work is crucial for improving the reliability of applications like autonomous driving, text-to-image generation, and image quality assessment, where consistent performance across diverse image conditions is paramount. The ultimate goal is to create more resilient and generalizable image processing systems.