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Spectral image fusion from compressive measurements

Vargas, Edwin and Espitia, Oscar and Arguello, Henry and Tourneret, Jean-Yves Spectral image fusion from compressive measurements. (2019) IEEE Transactions on Image Processing, 28 (5). 2271-2282. ISSN 1057-7149

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Official URL: https://doi.org/10.1109/TIP.2018.2884081


Compressive spectral imagers reduce the number of sampled pixels by coding and combining the spectral information. However, sampling compressed information with simultaneous high spatial and high spectral resolution demands expensive high-resolution sensors. This paper introduces a model allowing data from high spatial/low spectral and low spatial/high spectral resolution compressive sensors to be fused. Based on this model, the compressive fusion process is formulated as an inverse problem that minimizes an objective function defined as the sum of a quadratic data fidelity term and smoothness and sparsity regularization penalties. The parameters of the different sensors are optimized and the choice of an appropriate regularization is studied in order to improve the quality of the high resolution reconstructed images. Simulation results conducted on synthetic and real data, with different compressive sampling imagers, allow the quality of the proposed fusion method to be appreciated.

Item Type:Article
HAL Id:hal-02052028
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Other partners > Universidad Industrial de Santander - UIS (COLOMBIA)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Laboratory name:
Deposited On:06 Feb 2019 09:34

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