面向大气颗粒物干扰的图像清晰化算法研究与展望(特邀)  

Image clarification algorithms for atmospheric particulate matterinterference: research and prospects (invited)

作　　者：罗熙媛 相萌 刘严严 王姬 杨奎 韩平丽 王鑫[1] 刘峻成 刘倩倩 刘金鹏[1,2] 刘飞 LUO Xiyuan;XIANG Meng;LIU Yanyan;WANG Ji;YANG Kui;HAN Pingli;WANG Xin;LIU Juncheng;LIU Qianqian;LIU Jinpeng;LIU Fei(School of Optoelectronic Engineering,Xidian University,Xi’an 710071,China;Xi’an Key Laboratory of Computational Imaging,Xi’an 710071,China;National Key Laboratory of Electromagnetic Space Security,Tianjin 300308,China)

机构地区：[1]西安电子科技大学光电工程学院,陕西西安710071 [2]西安市计算成像重点实验室,陕西西安710071 [3]电磁空间安全全国重点实验室,天津300308

出　　处：《红外与激光工程》2024年第8期18-39,共22页Infrared and Laser Engineering

基　　金：国家自然科学基金项目(62205259,62075175,62105254);中国科学院空间精密测量技术重点实验室开放课题资助项目(B022420004);电磁空间安全全国重点实验室开放课题资助项目。

摘　　要：在恶劣天气条件下,大气中的颗粒物会造成图像失真,为光学成像应用带来挑战。图像去雾技术应运而生,成为了解决这一难题的关键。文中对近年来的去雾方法按照基于非物理模型、物理模型和深度学习的方法进行分类,并对每类方法中流行的去雾方法进行了阐述。非物理模型算法试图通过增强、融合等方式改善图像质量,但在复杂情境下表现不佳;物理模型算法通过对大气特性建模来复原图像,然而对气象条件的适应性仍有待提高;深度学习方法以其端到端的学习能力,为图像去雾带来了新的可能性,但面临数据和计算资源的挑战。通过对算法优缺点的比较,文中为未来去雾研究提供总结与帮助,预示着去雾技术在数字图像处理领域的重要性将不断增强。Significance The rapid development of optical imaging and image processing technology has created an urgent need to improve optical image quality in many application areas.Images acquired in complex environments,such as those affected by atmospheric pollution or underwater imaging,are often degraded by haze,scattering,absorption,and other factors.These issues result in the loss of image details,reduced contrast,color distortion,and other problems,which in turn affect the visibility of the image and the ability to extract information.Optical image dehazing algorithms aim to recover real scene information from images affected by atmospheric illumination,improving visual quality and information.They provide clearer and more realistic image information for various application scenes,promoting scientific research and applications in related fields.In the future,as algorithm technology continues to innovate and optimize,the field of optical image processing will experience broader applications and deeper development.Progress This paper summarizes and organizes recent dehazing and clarity methods by classifying them into non-physical model-based,physical model-based,and deep learning-based methods.It elaborates on popular dehazing methods in each category.Non-physical model-based clarity algorithms aim to enhance the clarity and viewing quality of images through image processing techniques that enhance the local details and contour features of images.Clarity algorithms enhance the fine structure and texture information in images through local contrast,edge,and detail enhancement.This improves the three-dimensional and realistic appearance of images.Clarity algorithms have various applications in digital photography,medical imaging,and industrial inspection.They can improve the accuracy of image diagnosis and analysis,and promote the development of related fields.Secondly,physical model-based algorithms are used to simulate the propagation process of light in the atmosphere and infer the depth information of obscured obj

关 键 词：图像去雾 物理模型 深度学习 

分 类 号：O436[机械工程—光学工程]