Realistic Blur

Realistic blur synthesis and removal are active research areas aiming to improve image quality and the performance of AI models processing images affected by blur. Current efforts focus on developing more accurate blur models that incorporate factors like camera motion, depth, and optical aberrations, often integrating these models with deep learning architectures such as convolutional neural networks (CNNs) and transformers to achieve superior deblurring performance. These advancements are crucial for applications ranging from medical imaging and histopathology analysis to video processing and general image enhancement, where accurate blur representation and removal are essential for reliable interpretation and analysis. The development of more realistic synthetic blur datasets is also a key focus to improve the training and generalization capabilities of deblurring models.