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Superresolution for UAV images via adaptive multiple sparse representation and its application to 3-D reconstruction / Muhammad Haris in IEEE Transactions on geoscience and remote sensing, vol 55 n° 7 (July 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 7 (July 2017) . - pp 4047 - 4058 Titre : Superresolution for UAV images via adaptive multiple sparse representation and its application to 3-D reconstruction Type de document : Article/Communication Auteurs : Muhammad Haris, Auteur ; Takuya Watanabe, Auteur ; Liu Fan, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 4047 - 4058 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] agriculture [Termes IGN] carte thématique [Termes IGN] drone [Termes IGN] image à haute résolution [Termes IGN] image multi sources [Termes IGN] rapport signal sur bruit [Termes IGN] reconstruction 3D [Termes IGN] série temporelle Résumé : (Auteur) We propose a superresolution (SR) algorithm based on adaptive sparse representation via multiple dictionaries for images taken by unmanned aerial vehicles (UAVs). The SR attainable through the proposed algorithm can increase the precision of 3-D reconstruction from UAV images, enabling the production of high-resolution images for constructing high-frequency time series and for high-precision digital mapping in agriculture. The basic idea of the proposed method is to use a field server or ground-based camera to take training images and then construct multiple pairs of dictionaries based on selective sparse representations to reduce instability during the sparse coding process. The dictionaries are classified on the basis of the edge orientation into five clusters: 0, 45, 90, 135, and nondirection. The proposed method is expected to reduce blurring, blocking, and ringing artifacts especially in edge areas. We evaluated the proposed and previous methods using peak signal-to-noise ratio, structural similarity, feature similarity, and computation time. Our experimental results indicate that the proposed method clearly outperforms other state-of-the-art algorithms based on qualitative and quantitative analysis. In the end, we demonstrate the effectiveness of our proposed method to increase the precision of 3-D reconstruction from UAV images. Numéro de notice : A2017-491 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2687419 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2687419 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86420 [article]