Computational imaging systems are novel cameras that are combinations of optics, sensors, electronics,and algorithms that jointly enable new approaches for smart visual sensing and perception. It has a wide variety of applications in consumer electronics, autonomous driving, robotics, remote sensing, medical imaging, human computer interaction, machine vision, and scientific imaging. This course will cover core ideas and advanced topics of computational imaging systems and algorithms, including camera and image sensor models, high dynamc range imaging, coded imaging systems (aperture, exposure, illumination), burst photography for low-light imaging, 3D imaging, plenoptic functions and light field, Neural Radiance Fields (NeRF), compressive sensing, neuromorphic imaging, optical neural network, and more. Emphasis is on novel hardware and system design of computational cameras, as well as solving inverse problems with classic optimization algorithms and modern end-to-end learning-based methods. Students will learn the core principles of many computational imaging systems and implement key optimization-based and learning-based algorithms to solve inverse problems.

Advisory note: It is preferred to have taken courses in deep learning and signal processing beforehand.