MS-VSTS consists of decomposing the data into a sparse multi-scale dictionary like wavelets or curvelets, and then applying a VST on the coefficients in order to get almost Gaussian stabilized coefficients. In addition, for the VST used in the method, the transformed data are asymptotically Gaussian. This method is based on a Variance Stabilizing Transform (VST), a transform which aims to stabilize a Poisson data set such that each stabilized sample has a quasi constant variance. This paper presents a new multiscale decomposition on the sphere for data with Poisson noise, called Multi-Scale Variance Stabilizing Transform on the Sphere (MS-VSTS). That is why we need a powerful Poisson noise removal method on the sphere which is efficient on low count Poisson data. The two main scientific ob jectives, the study of the Milky Way diffuse background and the detection of point sources, are complicated by the lack of photons. The Large Area Telescope (LAT), the main instrument of the Fermi Gamma-Ray Space Telescope, detects high energy gamma rays with energies from 20 MeV to more than 300 GeV. Poisson denoising on the sphere : Application to the Fermi gamma ray space telescope Films / Vidéos / Animations Photothèque Infographies Magazines, Newsletters Webdocumentaires Cours / MOOCS Sites grand public Expositions itinérantes Glossaire Résumé du preprint Irfu-10-57 Irfu-10-57
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