Computational Photography

开始时间: 待定 持续时间: Unknown

所在平台: Coursera

课程类别: 信息,技术与设计

大学或机构: Georgia Institute of Technology(佐治亚理工学院)

授课老师: Irfan Essa Anonymous1



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The goal of this course is to introduce you to the basics of how computation has impacted the entire workflow of photography (i.e., from how images are captured, manipulated and collaborated on, and shared).  

The course begins with a conceptualization of photography as drawing with light and the capturing of light to form images/videos.  You will learn about and understand how the optics and the sensor within a camera are generalized, as well as learn about and understand how the lighting and other aspects of the environment are also generalized through computation to capture novel images. 

Pre- and post-processing techniques used to manipulate and improve images will be discussed.  Activities in this course are selected to give you first hand experience with the power of the web and the Internet for both analyzing and sharing images.  

This course is interdisciplinary and draws upon concepts and principles from computer vision, computer graphics, image processing, mathematics and optics.

We look forward to your engagement and participation with both the course and its discussion forums.

About the TA
Denis Lantsman is the TA for the class. Denis is a graduate of Harvey Mudd College, and is currently finishing his MS in Machine Learning at Georgia Tech. He is responsible for managing the coursera site, monitoring the forums for student feedback, creating the homework assignments and quizzes, as well as recording weekly tutorials to help students with their programming.


Week 0 (Module 0):  Introductions with an overview of the course structure and content.  Topics covered in this module include a description of what is Computational Photography (i.e., whereby examples of dual photography and panoramas are described) and reasons for studying this emerging interdisciplinary field .

Week 1 (Module 1):  Overview of what is a digital image.  Topics covered in this module are image processing and filtering, with emphases placed upon point processes, smoothing, convolution, cross-correlation, gradients and edges.

Week 2 (Module 2):  Overview of cameras with emphases placed upon the pinhole camera and optics (e. g., lenses, focal length), exposure time and sensors.          

Week 3 (Module 3):  Feature detection, matching and correspondence. The panorama pipeline and some examples. HDR and tone mapping.

Week 4 (Module 4): Overview of light fields, texture synthesis, image retargeting, video, advanced topics.



课程图谱 2013-06-11 10:41 0 票支持; 0 票反对

课程名翻译为“计算摄影学”,有点拿不准,google了一下,发现果然有这个学科 ,真是无处不计算啊。CCF上还有一期专门介绍这个学科的文章,感兴趣的同学可以看看:计算摄影学简介


自1991年美国麻省理工学院的阿德尔森(Adelson)教授提出关于成像的全光函数描述方法以来, 人们不断致力于采用7维函数中的若干维组合来描 述视觉信息。实际上,传统的胶片或数字图像只是 全光函数的一个子集或者切片,对视觉信息的采 集 非常有限。广大研究者通过改变现有的成像设备, 立足更好地采集和恢复全光函数的某个或某几个维 度的信息,催生了一个全新的学科——计算摄影 学。其目的是突破现有数字成像的桎梏,使看不见 的内容看得见,看不清的内容看得清,看不全的内 容看得全。

计算摄影学是一门将计算机视觉、数字信号 处理、图形学等深度交叉的新兴学科,旨在结合计 算、数字传感器、光学系统和智能光照等技术,从成像机理上来改进传统相机,并将硬件设计与软件 计算能力有机结合,突破经典成像模型和数字相机 的局限性,增强或者扩展传统数字相机的数据采集 能力,全方位地捕捉真实世界的场景信息。

围绕全光函数各维度信息的获取,广大研究 者进行了多方面的探索,研究内容包括光场信息的 采集、高速运动对象的捕获、多光谱图像的拍摄与 融合、场景深度信息的获取等。自20世纪末,计算 摄影学迅猛发展,催生了若干专题会议和研讨会。 一些受到领域同行高度认可的国际会议也组织 相关 的专题研讨(special session),吸引了大批的研究 者。计算摄影学研究取得了一些令人兴奋的成果, 逐渐成为一个热门的研究领域。


wzyer 2013-05-17 08:31 0 票支持; 0 票反对


Deep Learning Specialization on Coursera


In this course you will learn about the basics of how computation has impacted the entire workflow of photography (i.e., from how images are captured, manipulated and collaborated on, and shared).


计算摄影学 摄影学 摄影 佐治亚理工学院



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