Information Processing
& Algorithms Laboratory

everything can be optimized

ABOUT

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.

NEWS


Winning Best Paper Award!
Featured Article in IEEE Signal Processing Magazine

Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing by V. Monga, Y. Li and Y. Eldar.
The article can be found here.

Dr. Monga Elevated to IEEE Fellow!

Monga's contributions to computationally efficient image analysis and restoration are responsible for his elevation to IEEE fellow. His elevation to IEEE fellow is the most recent of a list of research-based awards, including best paper awards from the IEEE, a National Science Foundation CAREER Award and induction into the National Academy of Inventors.
The news article can be found here.

IEEE TCI
Deep, Convergent, Unrolled Half-Quadratic Splitting For Image Deconvolution

We propose an unrolling technique that breaks the trade-off between retaining algorithm properties while simultaneously enhancing performance. We focus on image deblurring and unrolling the widely-applied Half-Quadratic Splitting (HQS) algorithm. We develop a new parametrization scheme which enforces layer-specific parameters to asymptotically approach certain fixed points. Through extensive experimental studies, we verify that our approach achieves competitive performance with state-of-the-art unrolled layer-specific learning and significantly improves over the traditional HQS algorithm. We further establish convergence of the proposed unrolled network as the number of layers approaches infinity, and characterize its convergence rate. Our experimental verification involves simulations that validate the analytical results as well as comparison with state-of-the-art non-blind deblurring techniques on benchmark datasets. The merits of the proposed convergent unrolled network are established over competing alternatives, especially in the regime of limited training.

BMVC 2023
Maturity-Aware Active Learning for Semantic Segmentation with Hierarchically-Adaptive Sample Assessment

An AL method that benefits from a hierarchical approach to define a multiview data distribution, which takes into account the different "sample" definitions jointly, hence able to select the most impactful segmentation pixels with comprehensive understanding. MADBAL also features a novel uncertainty formulation, where AL supporting modules are included to sense the features' maturity whose weighted influence continuously contributes to the uncertainty detection. In this way, MADBAL makes significant performance leaps even in the early AL stage, hence reducing the training burden significantly.

NEW! Handbook of Convex Optimization Methods in Imaging Science

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.

RESEARCH AREAS

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Medical & Computational Imaging
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Sparsity Constrained & Robust Time-series Signal Estimation
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Radar Signal Processing
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Regularized Deep Networks

KEY RESEARCH COLLABORATORS

Academia

Prof. Yonina Eldar (Weizmann Institute, Israel) and Prof. Trac Tran (Johns Hopkins University)

Government Labs

Dr. Muralidhar Rangaswamy (US Air Force Research Laboratory)
Dr. Nasser Nasrabadi (US Army Research Laboratory, now West Virginia University)

Industry

Dr. Raja Bala (Prev. Xerox PARC, Now Amazon)

KEY RESEARCH SPONSORS

Email
ipal.psu@gmail.com

Address
104 Electrical Engineering East,
University Park, PA 16802, USA

Lab Phone:
814-863-7810
814-867-4564