计算成像技术在信息复原及增强中的研究进展(特邀)
Comprehensive Review of Computational Imaging Techniques in Information Restoration and Enhancement(Invited)
贺天悦 1寇廷栋 1张启灿 1陈文静 1申俊飞1
作者信息
- 1. 四川大学电子信息学院,四川 成都 610065
- 折叠
摘要
计算成像是融合了光学设计、光学传感和图像处理的新兴技术领域,突破了传统成像技术获取信息的深度和广度限制,成为国际研究热点,是先进光学成像技术的重要发展方向.综合国内外文献和相关报道,以计算成像在信息复原及信息增强应用场景的技术发展为主线,结合新方法、新算法探讨各个子领域的主要进展,介绍端到端相机成像优化模型、衍射光学模型及基于可微光线追踪的复杂透镜模型等.近年来,无论是光学系统硬件加工还是图像处理算法都有着惊人的发展速度,多样化系统结构和先进算法的结合为计算成像提供了强大的发展动力,从人脸识别到物体检测,计算成像技术广泛涵盖了安防监控、医疗诊断、零售和娱乐等众多领域,相信未来也会在更多科学应用领域看到它的价值.
Abstract
Computational imaging,an interdisciplinary field that integrates optical design,optical sensing,and image processing,overcomes the limitations in the depth and scope of information acquisition associated with traditional imaging techniques.It has emerged as the focal point of international research efforts and represents an advanced trajectory for optical imaging technologies.This review includes insights from domestic and international academic literature.Futher,herein,the technological development of computational imaging applications in information restoration and enhancement scenarios is discussed.This review investigates primary advancements in various subdomains by exploring novel methods and algorithms.We also discuss various frameworks ranging from end-to-end camera-image-optimization models to diffractive optical models and ray-tracing-based lens models.Remarkable developments have recently been made in hardware fabrication and image processing algorithms,which have accelerated the evolution of computational imaging technologies.From applications in facial recognition to object detection,computational imaging technology is widely used in various domains,such as security surveillance,medical diagnostics,retail,and entertainment.The convergence of diverse system architectures with advanced algorithms can be further improved in near future by extending applications to an even broader spectrum of scientific domains.
关键词
计算成像/全链路/光信息编解码Key words
computational imaging/wholly configuration/optical encoding and decoding引用本文复制引用
基金项目
国家自然科学基金(62105227)
江西省重大科技研发专项(20224AAC01011)
四川省科技计划(2022YFS0113)
中国科协"青年人才托举工程"人才项目(2022QNRC001)
四川省天府峨眉计划人才项目(A0103602)
出版年
2024
NETL
NSTL
万方数据