Hassan Mansour
Mitsubishi Electric Research Laboratories
197 Papers
1K Citations
Hassan Mansour is an academic researcher from Mitsubishi Electric Research Laboratories. The author has contributed to research in topics: Radar imaging & Computer science. The author has an hindex of 24, co-authored 188 publications. Previous affiliations of Hassan Mansour include Mitsubishi & King Abdulaziz University.
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Papers
A Plug-and-Play Priors Approach for Solving Nonlinear Imaging Inverse Problems
TL;DR: This letter develops the fast iterative shrinkage/thresholding algorithm variant of PPP for model-based nonlinear inverse scattering and shows that the PPP approach is applicable beyond linear inverse problems.
288
Recovering Compressively Sampled Signals Using Partial Support Information
TL;DR: In this paper, the authors study the recovery conditions of weighted l1 minimization for signal reconstruction from compressed sensing measurements when partial support information is available, and they show that if at least 50% of the (partial) information is accurate, then weighted l 1 minimization is stable and robust under weaker sufficient conditions than the analogous conditions for standard l1 minimizing.
280
SparsePPG: Towards Driver Monitoring Using Camera-Based Vital Signs Estimation in Near-Infrared
Ewa Magdalena Nowara,Tim K. Marks,Hassan Mansour,Ashok Veeraraghavany +3 more
- 18 Jun 2018
TL;DR: A novel rPPG signal tracking and denoising algorithm (sparsePPG) is developed based on Robust Principal Components Analysis and sparse frequency spectrum estimation and a new dataset of face videos collected simultaneously in RGB and NIR is released.
Efficient matrix completion for seismic data reconstruction
Rajiv Kumar,Curt Da Silva,Okan Akalin,Aleksandr Y. Aravkin,Hassan Mansour,Benjamin Recht,Felix J. Herrmann +6 more
TL;DR: In this article, a low-rank optimization technique was proposed to recover the missing trace of seismic data from the source and receiver coordinates, where the original signal is low rank and the subsampling scheme increases the singular values of the matrix.
99
Fast Methods for Denoising Matrix Completion Formulations, with Applications to Robust Seismic Data Interpolation
TL;DR: This paper considers matrix completion formulations designed to hit a target data-fitting error level provided by the user, and proposes an algorithm called LR-BPDN that is able to exploit factorized formulations to solve the corresponding optimization problem.
86