Computational Imaging and Inverse Problems: Making the Invisible Visible

Computational imaging is a rapidly evolving interdisciplinary field awarded of many Nobel prizes. In computationalopticalimaging, digitalprocessing is employed in conjunction with an optical system toformimages. That is, images are computationally formed fromsome indirectmeasurements bysolving an inverse problem. Driven by advances insignal processing techniques and faster computing platforms, this approach continuously yields the development of next-generationimaging systems in consumerelectronics, defenseindustry, space physics, bioimaging and medicine. Theseimaging systemsenablenew forms of visual information, new imaging functionalities, reducedhardware complexity, and cost,as well ashigher resolution, that would be difficult, if not impossible, to achieve using traditional imaging. In this talk, first the fundamentals ofcomputational optical imaging will be describedand a unified treatment of the mathematical principles, inverse problems,and computational methods underlying the development of modernopticalimaging technologies will be provided. Afterwards, an overview of ongoing projects at METU Computational Imaging Lab will be presented with a focus on spectral imaging. In particular, aclass of novel spectral imaging techniques will be describedin detail. All of these involve distributing the imaging task between a novel optical system and a reconstruction algorithm. The optical systems take multiplexed measurementsusing diffractive lenses and coded apertures,andthen these measurements are usedwith areconstruction algorithmto digitally formthe spectral images. Compressive sensing theory, convex optimization, sparsity-and deep learning-basedimage reconstruction approaches are exploited for this purpose. The developed spectral imaging techniques not only enablehighspatial, spectral, and temporal resolutions that are beyond the reach of conventional techniques, but also allow reduced hardware complexityand cost.
Citation Formats
S. F. Öktem, “Computational Imaging and Inverse Problems: Making the Invisible Visible,” Koç Üniversitesi , İstanbul, Türkiye, 2019, p. 15, Accessed: 00, 2021. [Online]. Available: