The Information Processing and Algorithms Laboratory (iPAL) is directed by Prof. Vishal Monga. Graduate research in iPAL focuses on convex and non-convex optimization methods in learning, vision and signal processing. Our particular interest is in estimation frameworks where domain inspired prior knowledge is captured. This invariably leads to challenging optimization problems for which we develop new tractable solutions that facilitate a favorable performance-complexity trade-off.
Prof. Monga is serving as the Lead Guest Editor of the IEEE Journal of Selected Topics in Signal Processing - Special Issue on Domain Enriched Learning for Medical Imaging. Details about the Call for Paper can be found at IEEE JSTSP.
This book discusses imaging science and provides tools for solving image processing and computer vision problems using convex optimization methods. Throughout the handbook, the authors introduce topics on the most key aspects of image acquisition and processing that are based on the formulation and solution of novel optimization problems.
iPAL wins the New Trends in Image Restoration and Enhancement (NTIRE) Dehazing Challenges in CVPRW'19:
AtJ-DH ranks first in the NTIRE'19 Dehazing Challenges.
123-CEDH ranks second in the NTIRE'19 Dehazing Challenges.
Prof. Yonina Eldar (Weizmann Institute, Israel) and Prof. Trac Tran (Johns Hopkins University)
Dr. Muralidhar Rangaswamy (US Air Force Research Laboratory)
Dr. Nasser Nasrabadi (US Army Research Laboratory, now West Virginia University)
Dr. Raja Bala (Xerox PARC)