计算摄像学：核心、方法与应用  

Computational Photography:Keys,Methods and Applications

作　　者：索津莉[1] 刘烨斌[1] 季向阳[1] 戴琼海[1] 

机构地区：[1]清华大学清华国家信息实验室清华大学自动化系,北京100084

出　　处：《自动化学报》2015年第4期669-685,共17页Acta Automatica Sinica

基　　金：国家自然科学基金(61327902,61120106003,61171119)资助~~

摘　　要：针对现有计算机视觉、图形学、信号处理、数字图像处理、应用光学等领域无法通过现有成像模型与装置及计算方法获取足够目标场景信息的难题,计算摄像学研究提出新的成像机制与对应的计算重构方法,在光信号观测领域另辟蹊径,创新性地将视觉信息处理与计算前移至成像过程,从而极大地提高了信息优化计算的自由度,能够在维度、尺度与分辨率上实现质的突破,从而观测到传统成像系统"看不清"与"看不见"的场景信息.本文沿着计算摄像学思路、方法与目标三条主线,对国内外研究现状进行分析与综述,期望能够帮助读者更快地了解及进入相关研究.Current imaging mechanisms and systems cannot capture su-cient visual information of target ob jects/scenes in many fields, such as computer vision, graphics, signal processing, digital image processing, applied optics, etc. To address these challenges, computational photography has proposed new imaging mechanisms and corresponding reconstruction methods that bring the visual information processing forward to the acquisition process and largely raise the degree of freedom on information optimization. The computational acquisition approaches are able to breakthrough the bottlenecks in dimension, scale, and resolution, and thus can observe the scenes that cannot be captured clearly by traditional imaging systems. This review focuses on three main aspects of computational photography — strategy, approach, and target —and attempts to familiarize the readers with the studies in this field.

关 键 词：计算摄像学 全光函数 计算光路 高维高分辨率 计算重构 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]