EE6620
Computational Photography
Graduate (Spring
2016-2022)
Prerequisites: Linear Algebra, Probability,
Digital Signal Processing, Python
Syllabus
Computational photography studies problems about
image capture and processing that uses digital computation. For pictures
captured by traditional photography, it can alleviate some common
problems, e.g. image noise in low-light condition, blurred images for
long exposure time, over-exposure under sunlight. In addition to quality
improvement, it can also generate novel pictures for different
applications, such as panorama stitching, free-viewpoint synthesis,
digital refocusing, and video frame interpolation. This course
introduces computational photography in four different aspects:
image formation, optimization-based image processing, convolutional
neural networks, and selected topics.
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