真实场景图像去模糊:挑战与展望  

Real-world image deblurring:challenges and prospects

作　　者：王珮[1] 朱宇[1] 闫庆森[1] 孙瑾秋 张艳宁[1] Wang Pei;Zhu Yu;Yan Qingsen;Sun Jinqiu;Zhang Yanning(School of Computer Science and Engineering,Northwestern Polytechnical University,Xi'an 710072,China;School of Astronautics,Northwestern Polytechnical University,Xi'an 710072,China)

机构地区：[1]西北工业大学计算机学院,西安710072 [2]西北工业大学航天学院,西安710072

出　　处：《中国图象图形学报》2024年第12期3501-3528,共28页Journal of Image and Graphics

基　　金：国家自然科学基金项目(61901384,61871328,U19B2037)。

摘　　要：图像去模糊是计算机视觉的基础任务,对医学影像、监控摄像及卫星图像等领域具有重要意义。对于真实场景去模糊任务,由于场景内可能存在多个目标以及复杂的运动,成像过程容易受到许多外界因素的干扰,例如噪声、光照等,使图像去模糊问题更复杂。早期的研究主要针对仿真降质,但由于仿真模型受到多种假设限制,例如高斯噪声和全局一致运动等,难以在真实场景下展现出良好的复原效果。因此,越来越多的学者着手研究真实场景去模糊问题,以提升去模糊方法在现实生活中的使用价值。当前对真实场景下去模糊问题的综述性研究尚处于空白阶段,为此本文对真实场景去模糊任务进行了系统调研,分析其中存在的挑战,从降质模型的角度出发,由浅入深,由易到难,将真实场景下的去模糊问题拆解开,归纳为单一模糊去除方法、复合模糊去除方法以及真实场景下未知模糊去除方法,全方位描述了当前学术界在该问题上的研究内容和方法,总结和对比了各类方法的优缺点,阐述了阻碍复原性能进一步提升的难点问题,并对常用的一些数据集和评价指标进行了整理总结。最后,对真实场景去模糊任务的未来发展前景和研究热点进行了展望,并给出了可能的解决方法。Image deblurring is a fundamental task in computer vision that holds significant importance in various applications,such as medical imaging,surveillance cameras,and satellite imagery.Over the years,image deblurring has garnered much research attention,leading to the development of numerous dedicated methods.However,in real-world scenarios,the imaging process may be subject to various disturbances that can lead to complex blurring.Certain factors,such as inconsistent object motion,camera lens defocusing,pixel compression during transmission,and insufficient lighting,can lead to a range of intricate blurring phenomena that further amplify the deblurring challenges.In this case,image deblurring in real-world scenarios becomes a complex ill-posed problem,and conventional image deblurring based on simulated blurry degradations methods often falls short when confronted with these real-world deblurring challenges.These limitations are ascribed to the extent of assumptions on which these conventional methods depend.These assumptions include but are not limited to 1) traditional methods often assume that the noise in the image follows a Gaussian distribution;2) spatially invariant uniform blur assumption;and 3) independence of the blurring phenomena assumption.Although convenient for theoretical analysis and algorithm development,these assumptions prove to be restrictive when applied to the complex deblurring problems encountered in real-world scenarios.Consequently,there is a pressing need to conduct specialized research tailored to the challenges of real-world image deblurring and to enhance the effectiveness of image restoration methods.Real-world image deblurring is an intricate task that requires the development of innovative algorithms and techniques that are capable of accommodating the diversity of blurring factors and the complexities present in practical environments.This paper attempts to create unique deblurring solutions that can efficiently handle real-world scenarios and enhance the practical applicability

关 键 词：图像去模糊 真实场景 非均匀模糊 复合模糊 未知降质表征 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]