Poisson Noise Removal in Spherical Multichannel Images: Application to Fermi data

Jeremy Schmitt (1) , Jean-Luc Starck (1, 2) , Jalal M. Fadili (3) , Seth W. Digel (4)

1 AIM (UMR7158 / UMR_E_9005 / UM_112) - Astrophysique Interprétation Modélisation
2 DEDIP - Département d'Electronique, des Détecteurs et d'Informatique pour la Physique (ex SEDI)
3 Equipe Image - Laboratoire GREYC - UMR6072
4 SLAC - SLAC National Accelerator Laboratory

The aim of this chapter is to present a multi-scale representation for spherical data with Poisson noise called Multi-Scale Variance Stabilizing Transform on the Sphere (MS-VSTS) [14], combining the MS-VST [25] with various multi-scale transforms on the sphere (wavelets and curvelets) [22, 2, 3]. Section 1.2 presents some multi-scale transforms on the sphere. Section 1.3 introduces a new multi-scale representation for data with Poisson noise called MS-VSTS. Section 1.4 applies this representation to Poisson noise removal on Fermi data. Section 1.5 presents applications to missing data interpolation and source extraction. Section 1.6 extends the method to multichannel data.

Domaines: Traitement des images [eess.IV]; Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Jeremy Schmitt, Jean-Luc Starck, Jalal M. Fadili, Seth W. Digel. Poisson Noise Removal in Spherical Multichannel Images: Application to Fermi data. Way, Michael J. and Scargle, Jeffrey D. and Ali, Kamal M. and Srivastava, Ashok N. Advances in Machine Learning and Data Mining for Astronomy, Chapman and Hall, pp.183-212, 2012, 9781439841730. ⟨10.1201/b11822-14⟩. ⟨hal-00812518⟩