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Simultaneous intensity bias estimation and stripe noise removal in infrared images using the global and local sparsity constraints / Li Liu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 3 (March 2020) . - pp 1777 - 1789 Titre : Simultaneous intensity bias estimation and stripe noise removal in infrared images using the global and local sparsity constraints Type de document : Article/Communication Auteurs : Li Liu, Auteur ; Luping Xu, Auteur ; Houzhang Fang, Auteur Année de publication : 2020 Article en page(s) : pp 1777 - 1789 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse bivariée [Termes IGN] analyse comparative [Termes IGN] filtrage du bruit [Termes IGN] image infrarouge [Termes IGN] intensité lumineuse [Termes IGN] interpolation polynomiale [Termes IGN] itération [Termes IGN] optimisation (mathématiques) [Termes IGN] programmation par contraintes [Termes IGN] texture d'image Résumé : (Auteur) Infrared (IR) images are often contaminated by obvious intensity bias and stripes, which severely affect the visual quality and subsequent applications. It is challenging to eliminate simultaneously the mixed nonuniformity noise without blurring the fine-image details in low-textured IR images. In this article, we present a new model for simultaneous intensity bias correction and destriping through introducing two sparsity constraints. One is that model fit on the intensity bias should be as accurate as possible. A bivariate polynomial model is built to characterize the global smoothness of the intensity bias. The other constraint is that the unidirectional variational sparse model can concisely represent the direction characteristic of stripe noise. A computationally efficient numerical algorithm based on split Bregman iteration is used to solve the complex optimization problem. The proposed method is fundamentally different from the existing denoising techniques and simultaneously estimates the sharp image, intensity bias, and stripe components. Significant improvement on image quality is achieved on both simulated and real studies. Both qualitative and quantitative comparisons with the state-of-the-art correction methods demonstrate its superiority. Numéro de notice : A2020-089 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2948601 Date de publication en ligne : 18/11/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2948601 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94663 [article]