Computational photography is a field to study computational algorithms to overcome limitations of traditional cameras and to provide users new imaging applications. This field is a relatively new field emerged by the convergence of computer vision, image processing, and computer graphics. In this course, we first cover basic concepts to understand computational photography, and then several different topics as well as recent trends in computational photography. Topics will include cameras and optics, image formation, image and video processing, image restoration, image manipulation, image synthesis and so on. We will cover recent research papers in each topic and implement some of them to the degree that is possible.

Basic knowledge in linear algebra, probability, image processing, computer vision and computer graphics is highly recommended. Students are expected to be fluent in either C/C++ or python. Fluency in Matlab may also be helpful, but not required.

Homework 30%
Midterm exam 35%
Final exam 35%

Lecture slides will be posted online. There is no official textbook.
Recommended readings:
- Computer Vision: Algorithms and Applications, Richard Szeliski, Springer, 2010
 A free electrical version is available at http://szeliski.org/Book/
- Digital Image Processing, 3rd Edition, Gonzalez, and Woods, Prentice Hall, 2007
- Pattern Recognition and Machine Learning, Bishop, Springer, 2011
- Multiple View Geometry in Computer Vision, 2nd Edition, Hartley and Zisserman, Cambridge University Press, 2004

- Week 1: Course Intro, Cameras & Optics
- Week 2: Camera ISP & Image formation / Image filtering
- Week 3: No class - Chuseok
- Week 4: Image filtering / frequency domain
- Week 5: frequency domain / edge-aware filtering
- Week 6: Colors / image blending
- Week 7: Image warping / image alignment
- Week 8: Midterm exam
- Week 9: Morphology / global optimization
- Week 10: Deep learning basics / Generative adversarial networks
- Week 11: Image restoration
- Week 12: Image synthesis
- Week 13: Image manipulation
- Week 14: Image enhancement
- Week 15: HDR / Image matting
- Week 16: Final exam

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