Image Prior

Image priors are learned representations of typical image characteristics used to improve the accuracy and efficiency of various image processing tasks, such as denoising, inpainting, and super-resolution. Current research focuses on integrating these priors into deep learning models, particularly diffusion models and transformers, often incorporating techniques like patch-based training and conditional optimization to enhance performance and generalization. This work is significant because effective image priors enable robust and efficient solutions to challenging inverse problems in diverse applications, ranging from medical imaging to augmented reality.