3. 강의목표
Computational imaging has fueled the rapid developments of many application systems, including mobile phones, autonomous vehicles, VR/AR devices, consumer electronics, robots, microscopes, and telescopes. This course discusses essential concepts and critical applications of computational imaging. Specifically, we focus on digital photography, AI techniques for computational imaging, optics, human perception, inverse problems in computational imaging. Students will implement each concrete example in Python. Students will learn the cutting-edge research in computational imaging.
* 참고: 2024-2학기 학수번호(CSED520) 변경 예정
4. 강의선수/수강필수사항
This course does not require prerequisite even though knowledge on computer graphics, computer vision, machine learning, optics, and signal processing would be helpful.
5. 성적평가
Midterm: 50%
Final: 50%
6. 강의교재
| 도서명 |
저자명 |
출판사 |
출판년도 |
ISBN |
|
There is no textbook. All necessary materials will be provided in the course.
|
|
|
0000
|
|
7. 참고문헌 및 자료
Bhandari et al. (2022), Computational Imaging, MIT: https://imagingtext.github.io/
8. 강의진도계획
This course provides an introduction to computational imaging. Specifically, we cover the related topics in computer graphics, computer vision, machine learning, optics, and human perception.
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Course summary
1. Introduction to computational imaging
2. Digital photography
3. Optics
4. Human visual system
5. Material appearance and light transport
6. Inverse problems
7. Neural networks and optimization
8. Plenoptic imaging
9. End-to-end optimization of optics and reconstruction
10. Computational display
11. 장애학생에 대한 학습지원 사항
- 수강 관련: 문자 통역(청각), 교과목 보조(발달), 노트필기(전 유형) 등
- 시험 관련: 시험시간 연장(필요시 전 유형), 시험지 확대 복사(시각) 등
- 기타 추가 요청사항 발생 시 장애학생지원센터(279-2434)로 요청