基于深度学习的图像去模糊方法研究进展

Research Progress on Deep Learning-based Image Deblurring

近年来,随着便携、轻巧的数码成像设备的日益普及,人们获取图像的手段日益方便与灵活,数字图像在视频监控、医疗诊断、太空探测等领域起到了重要的作用。然而,在现有的成像过程中存在诸多问题,如相机的感光单元质量差、摄影者专业水平低、拍摄环境恶劣等,往往导致最终得到的图像含有明显的模糊以及噪声。如何使计算机自动地从模糊图像中把清晰的图像恢复出来,从而为其他的图像处理问题以及后续的计算机智能化分析提供高质量的图像,成为了亟待解决的问题。图像去模糊是典型的病态问题,解决该问题的常用方法主要包括基于统计先验建模和数据驱动的方法。然而,传统的统计先验建模的方法对清晰图像特征的刻画能力有限。而数据驱动的方法尤其是以深度学习为代表的方法依靠其强大的特征表示能力,为解决图像去模糊提供了一种新的、有效的方式。文中基于深度学习的图像去模糊方法,概述了目前图像去模糊方法的研究现状,分析了当前方法所面临的问题,并展望了图像去模糊方法的研究趋势。

With the increasing development of portable and smart digital imaging devices,the way to capture photos is more convenient and flexible.Digital images play an important role in video surveillance,medical diagnosis,space exploration,and so on.However,the captured images usually contain significant blur and noise due to the limited quality of the camera sensors,the skill of the photographers,and the imaging environments.How to restore the clear images from blurry ones so that they can facilitate the following intelligent analysis tasks is important but challenging.Image deburring is a classical ill-posed problem.Represented methods for this problem include the statistical prior-based methods and data-driven methods.However,conventional statistical prior-based methods have limited ability for modeling the inherent properties of the clear images.The data-driven methods,especially the deep learning methods,provide an effective way to solve image deblurring.This paper focuses on the deep learning-based image deblurring methods.It first introduces the research progress of the image deblurring problem,and then analyzes the challenges of the image deblurring problem.Finally,it discusses the research trends of the image deblurring problem.