Spectral image fusion from compressive measurements
| Autor: | Edwin Vargas, Oscar Espitia, Jean-Yves Tourneret, Henry Arguello |
|---|---|
| Přispěvatelé: | Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - INPT (FRANCE), Universidad Industrial de Santander - UIS (COLOMBIA), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Institut de Recherche en Informatique de Toulouse - IRIT (Toulouse, France), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Universidad Industrial de Santander [Bucaramanga] (UIS), CoMputational imagINg anD viSion (IRIT-MINDS), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Institut National Polytechnique (Toulouse) (Toulouse INP) |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
medicine.medical_specialty
Compressive sampling Pixel Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Iterative reconstruction Inverse problem Data fusion Remote sensing Sensor fusion Computer Graphics and Computer-Aided Design Spectral imaging Compressed sensing 0202 electrical engineering electronic engineering information engineering medicine Traitement du signal et de l'image 020201 artificial intelligence & image processing Spectral resolution Algorithm Image resolution [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing Software Sparse matrix |
| Zdroj: | IEEE Transactions on Image Processing IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2019, 28 (5), pp.2271-2282. ⟨10.1109/TIP.2018.2884081⟩ |
| ISSN: | 1057-7149 |
| DOI: | 10.1109/TIP.2018.2884081⟩ |
| Popis: | International audience; 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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