基于深度学习的光场图像重建与增强综述(特邀)  

Deep Learning-Based Light-Field Image Restoration and Enhancement:A Survey(Invited)

作　　者：肖泽宇 熊志伟 王立志 黄华[3] Xiao Zeyu;Xiong Zhiwei;Wang Lizhi;Huang Hua(Key Laboratory of Ministry of Education for Brain inspired Intelligent Perception and Cognition,School of Information Science and Technology,University of Science and Technology of China,Hefei 230027,Anhui,China;School of Computer Science and Technology,Beijing Institute of Technology,Beijing 100081,China;School of Artificial Intelligence,Beijing Normal University,Beijing 100875,China)

机构地区：[1]中国科学技术大学信息科学技术学院脑启发智能感知与认知教育部重点实验室,安徽合肥230027 [2]北京理工大学计算机学院,北京100081 [3]北京师范大学人工智能学院,北京100875

出　　处：《激光与光电子学进展》2024年第16期204-222,共19页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(62131003)。

摘　　要：光场能够完整捕捉三维空间中的光线信息,记录光线在不同位置和方向上的强度,这一特性使其能够精准地感知复杂动态环境,在生命科学、工业检测和虚拟现实等领域中有巨大的研究价值和应用潜力。在光场的拍摄、处理和传输过程中,由于设备限制和外部影响因素如物体运动、噪声、低光照和恶劣天气,光场图像往往存在失真和降质,严重影响图像质量并限制其后续应用。为此,研究人员针对光场图像的不同降质提出各种重建与增强算法。传统的光场图像重建与增强算法依赖于人工设计的先验知识,且算法设计复杂、效率低、泛化性差。随着深度学习的发展,光场图像重建与增强算法取得突破性进展,其性能和效率得到显著提高。介绍该领域相关的研究背景和光场表示,并针对不同的光场降质,概述和讨论其中的典型算法,内容涵盖空间与视角维度超分辨率重建、去噪、去模糊、去遮挡、去雨雾雪、去反射、低光增强等。此外,还概述光场图像重建与增强算法未来的挑战和发展前景。Light fields can completely capture light information in three-dimensional space,thus enabling the intensity of light at different positions and directions to be recorded.Consequently,complex dynamic environments can be perceived accurately,thus offering significant research value and application potential in fields such as life sciences,industrial inspection,and virtual reality.During the capture,processing,and transmission of light fields,limitations in equipment and external factors,such as object motion,noise,low lighting,and adverse weather conditions,can distort and degrade light-field images.This significantly compromises the quality of the images and restricts their further applications.Hence,researchers have proposed restoration and enhancement algorithms for various types of light-field degradations to improve the quality of light-field images.Classical light-field image restoration and enhancement algorithms rely on manually designed priors and exhibit disadvantages of high complexity,low efficiency,and subpar generalizability.Owing to the advancement of deep-learning technologies,significant development has been achieved in algorithms for light-field image restoration and enhancement,thus significantly improving their performance and efficiency.In this survey,we introduce the research background and representation of light fields as well as discuss the typical algorithms used for addressing different light-field degradations,with emphasis on spatial and angular dimension super-resolution,denoising,deblurring,occlusion removal,rain/haze/snow removal,reflection removal,and low-light enhancement.We conclude this survey by summarizing the future challenges and trends in the development of light-field image restoration and enhancement algorithms.

关 键 词：光场 光场图像重建 光场图像增强 深度学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]