NEW GRADUATE-LEVEL COURSE
EE 798 Theory of Remote Image Formation
A new graduate-level course on imaging and inverse problems is offered in the Spring semester. This course provides a unified treatment of the mathematical principles and the computational methods underlying the development of modern imaging technologies. The unifying theme is computational imaging, which is a rapidly evolving and interdisciplinary field awarded of many Nobel prizes.
Graduate and undergraduate students, who have an interest to gain further knowledge in the following subjects, are encouraged to participate:
- Multidimensional signals, transforms, and sampling, and their role in the development of various imaging and sensing modalities
- Broad view of modern imaging technologies as computational imaging systems
- Working principle of different types of imaging modalities for optical imaging, radar imaging, biomedical imaging, astronomical imaging, and spectral imaging
- The inverse problem framework and the challenges involved in an image formation (reconstruction) problem
- Integrated use of the analytical and numerical tools from linear algebra, estimation and optimization theory to solve real-world image reconstruction problems
- Commonly used image reconstruction algorithms in different imaging systems (such as for image deconvolution, phase retrieval, compressed sensing, and tomography)
- Wave propagation concepts, antenna systems, and ambiguity function from a signal processing perspective
The interdisciplinary field of imaging lies at the intersection of applied mathematics, physics, signal processing, estimation theory, optimization, and computer algorithms, as well as different application domains in remote sensing. For this reason, the students working in the fields of signal processing, biomedical engineering, computer engineering, communications, microwaves and antennas, and microelectronics can all benefit from the contents of the course. In addition, the course can be complementary to the graduate students from other departments including physics, computer engineering and geological engineering, as well as to students from Informatics Institute and Institute of Applied Mathematics, for which imaging and inverse problems are of importance.
If you have further questions regarding this course, feel free to contact Assist. Prof. Figen S. Oktem.