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Estimating Instrument Spectral Response Functions Using Sparse Representations and Quadratic Envelopes

El Haouari, Jihanne ; Carlsson, Marcus LU ; Tourneret, Jean Yves ; Wendt, Herwig ; Gaucel, Jean Michel and Pittet, Christelle (2025) 2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025 In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Abstract

The estimation of high resolution spectrometer Instrument Spectral Response Functions (ISRFs) is crucial because an imperfect knowledge of these functions can induce errors in the measurements. The state-of-the-art for this problem currently relies on the use of parametric models, which frequently lack flexibility to accurately model real-world ISRFs. To address this limitation, this paper proposes and investigates the use of sparse representations for modeling and estimating ISRFs, where the ISRFs are decomposed in a fixed dictionary of atoms. To estimate the sparse coefficient vector, a novel sparsity inducing regularization of the problem based on quadratic envelopes is studied and compared to the classical LASSO estimator and to a... (More)

The estimation of high resolution spectrometer Instrument Spectral Response Functions (ISRFs) is crucial because an imperfect knowledge of these functions can induce errors in the measurements. The state-of-the-art for this problem currently relies on the use of parametric models, which frequently lack flexibility to accurately model real-world ISRFs. To address this limitation, this paper proposes and investigates the use of sparse representations for modeling and estimating ISRFs, where the ISRFs are decomposed in a fixed dictionary of atoms. To estimate the sparse coefficient vector, a novel sparsity inducing regularization of the problem based on quadratic envelopes is studied and compared to the classical LASSO estimator and to a greedy method based on the Orthogonal Matching Pursuit (OMP) algorithm. Results for simulated ISRFs from the MicroCarb mission indicate that the proposed spectral representations yield excellent ISRF estimates, and that the use of quadratic envelopes can yield significantly better precision than competing methods.

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author
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organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Instrument Spectral Response Function (ISRF), LASSO, Orthogonal Matching Pursuit (OMP), quadratic envelope regularization, Sparse representations
host publication
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
series title
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
conference name
2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025
conference location
Hyderabad, India
conference dates
2025-04-06 - 2025-04-11
external identifiers
  • scopus:105009597096
ISSN
1520-6149
DOI
10.1109/ICASSP49660.2025.10890604
language
English
LU publication?
yes
id
38cdc0e3-1cb8-4d3c-ab68-4add777754cc
date added to LUP
2026-01-20 17:38:40
date last changed
2026-01-21 07:54:25
@inproceedings{38cdc0e3-1cb8-4d3c-ab68-4add777754cc,
  abstract     = {{<p>The estimation of high resolution spectrometer Instrument Spectral Response Functions (ISRFs) is crucial because an imperfect knowledge of these functions can induce errors in the measurements. The state-of-the-art for this problem currently relies on the use of parametric models, which frequently lack flexibility to accurately model real-world ISRFs. To address this limitation, this paper proposes and investigates the use of sparse representations for modeling and estimating ISRFs, where the ISRFs are decomposed in a fixed dictionary of atoms. To estimate the sparse coefficient vector, a novel sparsity inducing regularization of the problem based on quadratic envelopes is studied and compared to the classical LASSO estimator and to a greedy method based on the Orthogonal Matching Pursuit (OMP) algorithm. Results for simulated ISRFs from the MicroCarb mission indicate that the proposed spectral representations yield excellent ISRF estimates, and that the use of quadratic envelopes can yield significantly better precision than competing methods.</p>}},
  author       = {{El Haouari, Jihanne and Carlsson, Marcus and Tourneret, Jean Yves and Wendt, Herwig and Gaucel, Jean Michel and Pittet, Christelle}},
  booktitle    = {{ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings}},
  issn         = {{1520-6149}},
  keywords     = {{Instrument Spectral Response Function (ISRF); LASSO; Orthogonal Matching Pursuit (OMP); quadratic envelope regularization; Sparse representations}},
  language     = {{eng}},
  series       = {{ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings}},
  title        = {{Estimating Instrument Spectral Response Functions Using Sparse Representations and Quadratic Envelopes}},
  url          = {{http://dx.doi.org/10.1109/ICASSP49660.2025.10890604}},
  doi          = {{10.1109/ICASSP49660.2025.10890604}},
  year         = {{2025}},
}