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Single disperser design for coded aperture snapshot spectral imaging.
A single disperser spectral imager is presented that exploits recent theoretical work in the area of compressed sensing to achieve snapshot spectral imaging and can be used to capture spatiospectral information of a scene that consists of two balls illuminated by different light sources.
Single-shot compressive spectral imaging with a dual-disperser architecture.
A single-shot spectral imaging approach based on the concept of compressive sensing with primary features of two dispersive elements, arranged in opposition and surrounding a binary-valued aperture code, which results in easily-controllable, spatially-varying, spectral filter functions with narrow features.
Online Convex Optimization in Dynamic Environments
A dynamic mirror descent framework is described which addresses the challenge of adapting to nonstationary environments arising in real-world problems, yielding low theoretical regret bounds and accurate, adaptive, and computationally efficient algorithms which are applicable to broad classes of problems.
This is SPIRAL-TAP: Sparse Poisson Intensity Reconstruction ALgorithms—Theory and Practice
- Z. Harmany, R. Marcia, R. Willett
- Mathematics, Computer ScienceIEEE Transactions on Image Processing
- 24 May 2010
The optimization formulation considered in this paper uses a penalized negative Poisson log-likelihood objective function with nonnegativity constraints (since Poisson intensities are naturally nonnegative) for estimation of f* from y in an inverse problem setting.
Compressed Sensing Performance Bounds Under Poisson Noise
- M. Raginsky, R. Willett, Z. Harmany, R. Marcia
- Computer ScienceIEEE Transactions on Signal Processing
- 27 October 2009
It is shown that, as the overall intensity of the underlying signal increases, an upper bound on the reconstruction error decays at an appropriate rate, but that for a fixed signal intensity, the error bound actually grows with the number of measurements or sensors.
A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case
This paper characterize the norm required to realize a function as a single hidden-layer ReLU network with an unbounded number of units, but where the Euclidean norm of the weights is bounded, including precisely characterizing which functions can be realized with finite norm.
Improved Strongly Adaptive Online Learning using Coin Betting
A new parameter-free online learning algorithm for changing environments that outperforms state-of-the-art methods in learning with expert advice and metric learning scenarios and obtains a strongly adaptive regret bound.
Sequential Anomaly Detection in the Presence of Noise and Limited Feedback
- M. Raginsky, R. Willett, Corinne Horn, Jorge G. Silva, R. Marcia
- Computer ScienceIEEE Transactions on Information Theory
- 15 November 2009
At the heart of the proposed approach lie exponential-family models which can be used in a wide variety of contexts and applications, and which yield methods that achieve sublinear per-round regret against both static and slowly varying product distributions with marginals drawn from the same exponential family.
Poisson Noise Reduction with Non-local PCA
- J. Salmon, Z. Harmany, C. Deledalle, R. Willett
- PhysicsIEEE International Conference on Acoustics…
- 25 March 2012
A novel denoising algorithm for photon-limited images which combines elements of dictionary learning and sparse patch-based representations of images and reveals that, despite its conceptual simplicity, Poisson PCA-based Denoising appears to be highly competitive in very low light regimes.
Multiscale Poisson Intensity and Density Estimation
Nonparametric Poisson intensity and density estimation methods studied in this paper offer near minimax convergence rates for broad classes of densities and intensities with arbitrary levels of smoothness and it is demonstrated that platelet-based estimators in two dimensions exhibit similar near-optimal error convergence rates.