Jolley Hall, Room 309
SEAGLE: Robust Computational Imaging under Multiple Scattering
Ulugbek S. Kamilov
Computational Sensing Team
Mitsubishi Electric Research Laboratories
Majority of modern methods in high-resolution three-dimensional (3D) optical microscopy rely on linear scattering models that assume weakly scattering samples, making them inherently inaccurate for many applications. This places fundamental limits—in terms of resolution, penetration, and quality—on the imaging systems relying on such models. In this talk, we describe a new technique for computational imaging called SEAGLE that combines a nonlinear scattering model and a total variation (TV) regularized inversion algorithm. SEAGLE exploits an efficient representation of light scattering as a recursive neural network for formulating a fast, large-scale imaging algorithm. The key benefit of SEAGLE is its efficiency and stability, even for objects with large permittivity contrasts. SEAGLE is suitable for robust imaging under multiple scattering and has a potential to broadly impact 3D imaging of multicellular organisms such as biological tissue.
Ulugbek S. Kamilov is a Research Scientist in the Computational Sensing team at Mitsubishi Electric Research Laboratories (MERL), Cambridge, MA, USA. Dr. Kamilov obtained his B.Sc. and M.Sc. in Communication Systems, and Ph.D. in Electrical Engineering from the École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, in 2008, 2011, and 2015, respectively. In 2007, he was an Exchange Student at Carnegie Mellon University (CMU), Pittsburgh, PA, USA, in 2010, a Visiting Student at Massachusetts Institute of Technology (MIT), Cambridge, MA, USA, and in 2013, a Visiting Student Researcher at Stanford University, Stanford, CA, USA.
Dr. Kamilov's research focus is computational imaging with an emphasis on the development and analysis of large-scale computational techniques for biomedical and industrial applications. His research interests cover imaging through scattering media, multimodal imaging, optical microscopy, and subsurface imaging. He has co-authored 17 journal and 32 conference publications in these areas. His Ph.D. thesis work on Learning Tomography (LT) was selected as a finalist for EPFL Doctorate Awards 2016 and was featured in the "News and Views" section of the Nature magazine. Since 2016, Dr. Kamilov is a member IEEE Special Interest Group on Computational Imaging.