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Interferometric phase image estimation via sparse coding in the complex domain / Hao Hongxing in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 5 (mai 2015) . - pp 2587 - 2602 Titre : Interferometric phase image estimation via sparse coding in the complex domain Type de document : Article/Communication Auteurs : Hao Hongxing, Auteur ; José M. Bioucas-Dias, Auteur ; Vladimir Katkovnik, Auteur Année de publication : 2015 Article en page(s) : pp 2587 - 2602 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] apprentissage dirigé [Termes IGN] état de l'art [Termes IGN] filtrage du bruit [Termes IGN] image radar moirée [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] matrice creuse [Termes IGN] phase [Termes IGN] programmation par contraintes [Termes IGN] régression [Termes IGN] restauration d'image Résumé : (auteur) This paper addresses interferometric phase image estimation, i.e., the estimation of phase modulo-2π images from sinusoidal 2π-periodic and noisy observations. These degradation mechanisms make interferometric phase image estimation a quite challenging problem. We tackle this challenge by reformulating the true estimation problem as a sparse regression, often termed sparse coding, in the complex domain. Following the standard procedure in patch-based image restoration, the image is partitioned into small overlapping square patches, and the vector corresponding to each patch is modeled as a sparse linear combination of vectors, termed the atoms, taken from a set called dictionary. Aiming at optimal sparse representations, and thus at optimal noise removing capabilities, the dictionary is learned from the data that it represents via matrix factorization with sparsity constraints on the code (i.e., the regression coefficients) enforced by the ℓ1 norm. The effectiveness of the new sparse-coding-based approach to interferometric phase estimation, termed the SpInPHASE, is illustrated in a series of experiments with simulated and real data where it outperforms the state-of-the-art. Numéro de notice : A2015-630 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2361919 En ligne : https://doi.org/10.1109/TGRS.2014.2361919 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78118 [article]

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