Restoration of SAR images using recovery of discontinuities and non-linear optimization

Florence Tupin; Marc Sigelle; Ammar Chkeif; Jean Pierre Vdran

doi:10.1007/3-540-62909-2_73

Restoration of SAR images using recovery of discontinuities and non-linear optimization

Florence Tupin
, Marc Sigelle
, Ammar Chkeif
, Jean Pierre Vdran

Ecole Nationale Supérieure des Télécoms

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

Résumé

In this paper, we study the behaviour of contour recovery when filtering radar images. We start from recent methods lying on an equivalence scheme between implicit and explicit boundary processes in image restoration [1, 2]. Here we extend them to the processing of synthetic aperture radar (SAR) images. First we set up a general bayesian frame enabling recovery of discontinuities in such restoration methods. Then we exhibit an extension of the Geman-Reynolds-Charbonnier theorem allowing convenient filtering of SAR images. Due to the high dynamics of radar ERS-1 images, a deterministic algorithm is proposed integrating different statistical hypotheses for observation and regularization parts. Besides, we use a well-adapted SAR edge detector instead of the usual gradient in the boundary estimation step of an iterative boundary/ intensity restoration algorithm. Intensities are then estimated with a deterministic non-linear method. Finally, the particular behaviour or radar statistics (X law) lead us to define a new potential function adapted to speckle regularization while respecting region discontinuities.

langue originale	Anglais
titre	Energy Minimization Methods in Computer Vision and Pattern Recognition - International Workshop EMMCVPR 1997, Proceedings
rédacteurs en chef	Edwin R. Hancock, Marcello Pelillo
Editeur	Springer Verlag
Pages	67-82
Nombre de pages	16
ISBN (imprimé)	3540629092, 9783540629092
Les DOIs	https://doi.org/10.1007/3-540-62909-2_73
état	Publié - 1 janv. 1997
Modification externe	Oui
Evénement	International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 1997 - Venice, Italie Durée: 21 mai 1997 → 23 mai 1997

Série de publications

Nom	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	1223
ISSN (imprimé)	0302-9743
ISSN (Electronique)	1611-3349

Une conférence

Une conférence	International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 1997
Pays/Territoire	Italie
La ville	Venice
période	21/05/97 → 23/05/97

Accès au document

10.1007/3-540-62909-2_73

Contient cette citation

Tupin, F., Sigelle, M., Chkeif, A., & Vdran, J. P. (1997). Restoration of SAR images using recovery of discontinuities and non-linear optimization. Dans E. R. Hancock, & M. Pelillo (eds.), Energy Minimization Methods in Computer Vision and Pattern Recognition - International Workshop EMMCVPR 1997, Proceedings (p. 67-82). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol 1223). Springer Verlag. https://doi.org/10.1007/3-540-62909-2_73

Tupin, Florence ; Sigelle, Marc ; Chkeif, Ammar et al. / Restoration of SAR images using recovery of discontinuities and non-linear optimization. Energy Minimization Methods in Computer Vision and Pattern Recognition - International Workshop EMMCVPR 1997, Proceedings. Editeur / Edwin R. Hancock ; Marcello Pelillo. Springer Verlag, 1997. p. 67-82 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Restoration of SAR images using recovery of discontinuities and non-linear optimization",

abstract = "In this paper, we study the behaviour of contour recovery when filtering radar images. We start from recent methods lying on an equivalence scheme between implicit and explicit boundary processes in image restoration [1, 2]. Here we extend them to the processing of synthetic aperture radar (SAR) images. First we set up a general bayesian frame enabling recovery of discontinuities in such restoration methods. Then we exhibit an extension of the Geman-Reynolds-Charbonnier theorem allowing convenient filtering of SAR images. Due to the high dynamics of radar ERS-1 images, a deterministic algorithm is proposed integrating different statistical hypotheses for observation and regularization parts. Besides, we use a well-adapted SAR edge detector instead of the usual gradient in the boundary estimation step of an iterative boundary/ intensity restoration algorithm. Intensities are then estimated with a deterministic non-linear method. Finally, the particular behaviour or radar statistics (X law) lead us to define a new potential function adapted to speckle regularization while respecting region discontinuities.",

author = "Florence Tupin and Marc Sigelle and Ammar Chkeif and Vdran, \{Jean Pierre\}",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 1997.; International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 1997 ; Conference date: 21-05-1997 Through 23-05-1997",

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Tupin, F, Sigelle, M, Chkeif, A & Vdran, JP 1997, Restoration of SAR images using recovery of discontinuities and non-linear optimization. Dans ER Hancock & M Pelillo (eds), Energy Minimization Methods in Computer Vision and Pattern Recognition - International Workshop EMMCVPR 1997, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), VOL. 1223, Springer Verlag, p. 67-82, International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 1997, Venice, Italie, 21/05/97. https://doi.org/10.1007/3-540-62909-2_73

Restoration of SAR images using recovery of discontinuities and non-linear optimization. / Tupin, Florence; Sigelle, Marc; Chkeif, Ammar et al.
Energy Minimization Methods in Computer Vision and Pattern Recognition - International Workshop EMMCVPR 1997, Proceedings. Ed. / Edwin R. Hancock; Marcello Pelillo. Springer Verlag, 1997. p. 67-82 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol 1223).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

TY - GEN

T1 - Restoration of SAR images using recovery of discontinuities and non-linear optimization

AU - Tupin, Florence

AU - Sigelle, Marc

AU - Chkeif, Ammar

AU - Vdran, Jean Pierre

N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 1997.

PY - 1997/1/1

Y1 - 1997/1/1

N2 - In this paper, we study the behaviour of contour recovery when filtering radar images. We start from recent methods lying on an equivalence scheme between implicit and explicit boundary processes in image restoration [1, 2]. Here we extend them to the processing of synthetic aperture radar (SAR) images. First we set up a general bayesian frame enabling recovery of discontinuities in such restoration methods. Then we exhibit an extension of the Geman-Reynolds-Charbonnier theorem allowing convenient filtering of SAR images. Due to the high dynamics of radar ERS-1 images, a deterministic algorithm is proposed integrating different statistical hypotheses for observation and regularization parts. Besides, we use a well-adapted SAR edge detector instead of the usual gradient in the boundary estimation step of an iterative boundary/ intensity restoration algorithm. Intensities are then estimated with a deterministic non-linear method. Finally, the particular behaviour or radar statistics (X law) lead us to define a new potential function adapted to speckle regularization while respecting region discontinuities.

AB - In this paper, we study the behaviour of contour recovery when filtering radar images. We start from recent methods lying on an equivalence scheme between implicit and explicit boundary processes in image restoration [1, 2]. Here we extend them to the processing of synthetic aperture radar (SAR) images. First we set up a general bayesian frame enabling recovery of discontinuities in such restoration methods. Then we exhibit an extension of the Geman-Reynolds-Charbonnier theorem allowing convenient filtering of SAR images. Due to the high dynamics of radar ERS-1 images, a deterministic algorithm is proposed integrating different statistical hypotheses for observation and regularization parts. Besides, we use a well-adapted SAR edge detector instead of the usual gradient in the boundary estimation step of an iterative boundary/ intensity restoration algorithm. Intensities are then estimated with a deterministic non-linear method. Finally, the particular behaviour or radar statistics (X law) lead us to define a new potential function adapted to speckle regularization while respecting region discontinuities.

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DO - 10.1007/3-540-62909-2_73

M3 - Conference contribution

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SN - 3540629092

SN - 9783540629092

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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EP - 82

BT - Energy Minimization Methods in Computer Vision and Pattern Recognition - International Workshop EMMCVPR 1997, Proceedings

A2 - Hancock, Edwin R.

A2 - Pelillo, Marcello

PB - Springer Verlag

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Y2 - 21 May 1997 through 23 May 1997

ER -

Tupin F, Sigelle M, Chkeif A, Vdran JP. Restoration of SAR images using recovery of discontinuities and non-linear optimization. Dans Hancock ER, Pelillo M, rédacteurs en chef, Energy Minimization Methods in Computer Vision and Pattern Recognition - International Workshop EMMCVPR 1997, Proceedings. Springer Verlag. 1997. p. 67-82. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/3-540-62909-2_73

Restoration of SAR images using recovery of discontinuities and non-linear optimization

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