Blur Attack

Blur attack research focuses on mitigating the detrimental effects of image and video blur, stemming from various sources like camera motion, defocus, and rolling shutter, improving image and video quality and enabling accurate analysis. Current research employs deep learning models, particularly transformers and diffusion models, along with novel techniques like blur-to-blur conversion and multi-modal blur decomposition, to achieve deblurring and related tasks such as depth estimation and pose tracking. These advancements have significant implications for diverse fields, including medical imaging (e.g., virtual Gram staining), video restoration, and forensic analysis, by enhancing the accuracy and reliability of image-based analyses.