Poisson denoising on the sphere

J. Schmitt; J. L. Starck; J. Fadili; I. Grenier; J. M. Casandjian

doi:10.1117/12.824446

4 September 2009 Poisson denoising on the sphere

J. Schmitt, J. L. Starck, J. Fadili, I. Grenier, J. M. Casandjian

Proceedings Volume 7446, Wavelets XIII; 74461A (2009) https://doi.org/10.1117/12.824446
Event: SPIE Optical Engineering + Applications, 2009, San Diego, California, United States

Abstract

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.

Citation Download Citation

J. Schmitt, J. L. Starck, J. Fadili, I. Grenier, and J. M. Casandjian "Poisson denoising on the sphere", Proc. SPIE 7446, Wavelets XIII, 74461A (4 September 2009); https://doi.org/10.1117/12.824446

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available