In the scope of the Fermi mission, Poisson noise removal should improve data quality and make source detection
easier. This paper presents a method for Poisson data denoising on sphere, called Multi-Scale Variance Stabilizing
Transform on Sphere (MS-VSTS). 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 an (asymptotically) constant
variance. In addition, for the VST used in the method, the transformed data are asymptotically Gaussian. Thus,
MS-VSTS consists in decomposing the data into a sparse multi-scale dictionary (wavelets, curvelets, ridgelets...),
and then applying a VST on the coefficients in order to get quasi-Gaussian stabilized coefficients. In this present
article, the used multi-scale transform is the Isotropic Undecimated Wavelet Transform. Then, hypothesis tests
are made to detect significant coefficients, and the denoised image is reconstructed with an iterative method
based on Hybrid Steepest Descent (HST). The method is tested on simulated Fermi data.
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