Ultrahigh-Resolution Computational Spectral Imaging With Learned Reconstruction

Sep
10

Ultrahigh-Resolution Computational Spectral Imaging With Learned Reconstruction

Farzad Kamalabadi, University of Illinois at Urbana-Champaign (UIUC)

10:30 a.m., September 10, 2026   |   258 Fitzpatrick Hall of Engineering

Spectral imaging—the simultaneous imaging and spectroscopy of a radiating scene—is a fundamental diagnostic technique in the physical sciences with widespread application. However, conventional spectral imaging techniques face limitations on the attainable spatial, temporal, and spectral resolutions imposed by their reliance on purely physical measurement systems such as two-dimensional detectors which are intrinsically limited in capturing inherently three-dimensional data.

Farzad Kamalabadi

Farzad Kamalabadi,
University of Illinois at
Urbana-Champaign (UIUC)

Similarly, conventional remote sensing systems rely on reflective optics which are not naturally scalable, as the desired increase in angular resolution requires larger apertures which cannot be manufactured with sufficient accuracy to attain the diffraction limit.

This talk highlights a class of image formation techniques that overcome the spectral, temporal, and spatial resolution limitations of conventional spectral imaging systems. Each development is based on the computational imaging paradigm, which involves distributing the spectral imaging task between a physical and a computational system and then digitally forming images of interest from multiplexed measurements by means of solving an inverse problem. The presentation will cover the fundamental theory and principles, as well as the concurrent technological advances required for such imaging systems, and the ongoing efforts toward demonstrating such systems for space remote sensing.

Farzad Kamalabadi is the Kung Chie and Margaret Yeh Endowed Professor in Electrical
and Computer Engineering and Professor of Statistics at the University of Illinois at
Urbana-Champaign (UIUC), where he has been on the faculty since 2000. He is also a
Professor at UIUC’s Coordinated Science Laboratory, where he leads a research
program at the nexus of remote sensing, signal processing, statistical learning theory,
dynamical systems, and space science. From 2010 to 2012, he served as a Program
Director at the National Science Foundation (NSF). Dr. Kamalabadi has held visiting
positions as an INRIA Visiting Professor in Sophia Antipolis, France (2008–2009), a
NASA Faculty Fellow at the Jet Propulsion Laboratory, Caltech (2003), and a Visiting
Fellow at SRI International (2002).