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.
Abstract
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.
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