Sparse component separation from Poisson measurements
Blind source separation (BSS) aims at recovering signals from mixtures. This problem has been extensively studied in cases where the mixtures are contaminated with additive Gaussian noise. However, it is not well suited to describe data that are corrupted with Poisson measurements such as in low photon count optics or in high-energy astronomical imaging (e.g. observations from the Chandra or Fermi telescopes). To that purpose, we propose a novel BSS algorithm coined pGMCA that specifically tackles the blind separation of sparse sources from Poisson measurements.
Code (0)
등록된 구현이 없습니다.
Tasks
blind source separationSimilar Papers 제목 키워드 기반
Incorporating Prior Information in Compressive Online Robust Principal Component Analysis
We consider an online version of the robust Principle Component Analysis (PCA), which arises naturally in time-varying source separations such as video foreground-background separation. This paper proposes a compressive …
Extreme Compressed Sensing of Poisson Rates from Multiple Measurements
Compressed sensing (CS) is a signal processing technique that enables the efficient recovery of a sparse high-dimensional signal from low-dimensional measurements. In the multiple measurement vector (MMV) framework, a se…
compressed sensingCompressive Online Robust Principal Component Analysis with Optical Flow for Video Foreground-Background Separation
In the context of online Robust Principle Component Analysis (RPCA) for the video foreground-background separation, we propose a compressive online RPCA with optical flow that separates recursively a sequence of frames i…
Optical Flow EstimationNeighbor Embedding for High-Dimensional Sparse Poisson Data
Across many scientific fields, measurements often represent the number of times an event occurs. For example, a document can be represented by word occurrence counts, neural activity by spike counts per time window, or o…
Dimensionality ReductionMinimax Optimal Sparse Signal Recovery with Poisson Statistics
We are motivated by problems that arise in a number of applications such as Online Marketing and Explosives detection, where the observations are usually modeled using Poisson statistics. We model each observation as a P…
DecoderMarketing