Abstract: Image deblurring is a classic problem in computer vision,aiming to recover sharp visual information from blurry input images or videos.Blur is often caused by factors such as camera misfocus,camera shake,or fast-moving objects.Traditional deblurring methods typically model the task as a deconvolution problem,treating the blurry image as the convolution of a sharp image and a blur kernel.However,these methods face limitations when dealing with complex or non-ideal blur types.In recent years,deep learning-based regression methods have made significant breakthroughs.These approaches leverage architectures such as Convolutional Neural Networks(CNNs) and Transformers to learn the mapping between blurry and sharp images,enabling effective handling of complex blur scenarios without explicit modeling of the blur kernel.Additionally,generative deep learning methods,such as Generative Adversarial Networks(GANs) and Diffusion models,have shown considerable potential in the deblurring field.Generative AI,by modeling and learning the image detail generation process,not only effectively removes blur but also generates high-quality images with fine textures,demonstrating superior performance in challenging blur scenarios.This paper first introduces the characteristics of image blur and outlines common deblurring tasks and evaluation metrics.It then delves into the fundamental architectures and training methods of deblurring models,providing a comparative analysis of representative state-of-the-art deblurring models.Finally,the paper explores potential future research directions in the field of image deblurring.

Key words: Image deblurring, Deep learning, Deconvolution, Autoregression model, Generative artificial intelligence