1. 斯坦福大学 课程EE367/CS448I

https://web.stanford.edu/class/ee367/ 课程内容有: Introduction and fast forward: overview of class, logistics, discussion of project ideas The human visual system:perception of color, depth, contrast, resolution Digital photography I:optics, aperture, depth of field, exposure, noise, sensors Digital photography II:image processing pipeline Sampling, Linear Systems:review of sampling, regularized linear systems Deconvolution:inverse filtering, Wiener filtering, total variation, ADMM Burst photography:HDR, tone mapping, super-resolution, flash/no-flash, multi-flash Light field photography:camera arrays, lytro, coded masks, refocus, fourier slice theorem Coded computational photography:extended depth of field, motion invariance, flutter shutter Noise: signal independent noise, signal-dependent noise, image reconstruction with noise Compressive imaging:single pixel camera, compressive sensing, compressive hyperspectral imaging, compressive light field imaging Computational illumination and light transport:Structured illumination, photometric stereo, shape from specularities, optical computing Introduction to computational microscopy:fluorescence, 3D microscopy, confocal, light field, light sheet, two-photon, etc Advanced Optimization in Computational Imaging Displays blocks:LCDs, SLMs, OLEDs, stereo displays, light field displays Computational displays:HDR displays, projection displays, vision-correcting displays, volumetric displays Wearable displays:head-mounted displays (HMDs), virtual reality (VR), augmented reality (AR)

2. 台大庄泳浴教授 digital visual effect

https://www.csie.ntu.edu.tw/~cyy/courses/vfx/10spring/lectures/,可以下载ppt或pdf课件。 课程组织为: 02/24 introduction 03/03 cameras 03/10 HDR Imaging 03/17 tone mapping 03/17 image morphing 03/24 features 03/31 image stitching 04/14 motion estimation 04/21 camera calibration 04/28 structure from motion 05/05 matting and compositing 05/12 Image-based modeling 05/26 3D Photography 06/02 Computational Photography 06/09 Computational Photography 06/09 IBL and faces

3.一个关于各类图像算法汇总的链接

http://www.efg2.com/Lab/Library/ImageProcessing/Algorithms.htm

4. Gerard de Hann教授的课程:Video processing for Multimedia systems

http://www.es.ele.tue.nl/~dehaan/slides/

5. Kayvon Fatahalian教授的课程,之前在CMU执教,后转到斯坦福

个人主页:http://graphics.stanford.edu/~kayvonf/ 2011年在CMU教过课程:Graphics and Imaging Architectures,课程中涉及到Digital Camera Image Processing Pipeline http://www.cs.cmu.edu/afs/cs.cmu.edu/academic/class/15869-f11/www/

6.评价图像质量的一些指标(孙推荐)

http://www.imatest.com/docs/iqfactors/

7.基于C#的图像处理算法(链接为平场矫正C#代码)

https://github.com/accord-net/framework/blob/master/Sources/Accord.Imaging/AForge.Imaging/Filters/IlluminationCorrection/FlatFieldCorrection.cs

8.Practical vignetting correction method for digital camera with measurement of surface luminance distribution

https://link.springer.com/article/10.1007/s11760-016-0941-2

9.镜头选择

https://www.baslerweb.com/en/vision-campus/vision-systems-and-components/find-the-right-lens/

10.相机硬件,软件的基本知识

https://www.keyence.com/ss/products/vision/visionbasics/

11.Andrew Ng课程笔记

https://web.stanford.edu/class/cs294a/handouts.html http://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=MachineLearning

12 PDF机器学习资料

http://cmuems.com/excap/readings/forsyth-ponce-computer-vision-a-modern-approach.pdf

13.Testing display defects (mura) with Imatest Blemish Detect,来自imatest网站

http://www.imatest.com/docs/testing-display-defects/

14.改进的分水岭算法,检测mura

https://www.worldscientific.com/doi/abs/10.1142/9789813146426_0036

15.有趣的公众号,了解数学

https://mp.weixin.qq.com/s?__biz=MzA5ODUxOTA5Mg==&mid=402635808&idx=1&sn=1ebd4563d47f230ab8d252f23ceb0a04&scene=21#wechat_redirect https://mp.weixin.qq.com/s?__biz=MzA5ODUxOTA5Mg==&mid=402678173&idx=1&sn=6904c7c57dc2601f0351212710c352a8&scene=21#wechat_redirect https://mp.weixin.qq.com/s?__biz=MzA5ODUxOTA5Mg==&mid=402339934&idx=1&sn=e91a04ad46d0d373c910dfe38aceb93e&scene=21#wechat_redirect