Rebecca Willett Department of Electrical and Computer Engineering University of Wisconsin - Madison "Multiscale Photon-Limited Image Analysis" Thursday March 10th, 12:20-1:10, Lind 409

Abstract
Many critical scientific and engineering applications rely upon the accurate reconstruction of spatially or temporally distributed phenomena from photon-limited data. However, a number of information processing challenges arise routinely in these problems: Sensing is often indirect in nature, such as tomographic projections in medical imaging, resulting in complicated inverse reconstruction problems. Limited system resources, such as data acquisition time and image storage requirements, lead to complex tradeoffs between communications, sensing and processing. Furthermore, the measurements are often "noisy" due to low photon counts. In addition, the behavior of the underlying photon intensity functions can be very rich and complex, and consequently difficult to model a priori. All of these issues combine to make accurate reconstruction a complicated task, involving a myriad of system-level and algorithm tradeoffs.

In this talk, I will demonstrate that nonparametric multiscale reconstruction methods can overcome all the challenges above and provide a theoretical framework for assessing tradeoffs between reconstruction accuracy and system resources. First, the theory supporting these methods facilitates characterization of fundamental performance limits. Examples include lower bounds on the best achievable error performance in photon-limited image reconstruction and upper bounds on the data acquisition time required to achieve a target reconstruction accuracy. Second, existing reconstruction methods can often be enhanced with multiscale techniques, resulting in significant improvements in a number of application domains. Underlying these methods are ideas drawn from the theory of multiscale analysis, statistical learning, nonlinear approximation theory, and iterative reconstruction algorithms. I will demonstrate the effectiveness of the theory and methods in several important applications, including superresolution imaging and medical image reconstruction.

Bio
Rebecca Willett is a graduate student in the Electrical and Computer Engineering Department at Rice University. In addition to studying at Rice, Ms. Willett has worked as a Fellow of the Institute for Pure and Applied Mathematics at UCLA, as a visiting researcher at the University of Wisconsin-Madison and the French National Institute for Research in Computer Science and Control (INRIA), and as a member of the Applied Science Research and Development Laboratory at GE Medical Systems (now GE Healthcare). She is a recipient of the National Science Foundation Graduate Research Fellowship, the Rice University Presidential Scholarship, and the Society of Women Engineers Caterpillar Scholarship. Her research interests include signal processing and communications with applications in medical imaging, astrophysics, and wireless sensor networks. Additional information, including publications and software, are available online at http://www.ece.rice.edu/~willett/.