Variational algorithms to remove stationary noise. Application to SPIM imaging.

Jérôme Fehrenbach (1) , Corinne Lorenzo (2) , Pierre Weiss (1)

1 IMT - Institut de Mathématiques de Toulouse UMR5219
2 ITAV - Institut des Technologies Avancées en sciences du Vivant

In the present paper, a framework and an algorithm are presented in order to remove stationary noise from images. This algorithm can be seen both as a restoration method in a Bayesian framework and as a cartoon+texture decomposition method. In numerous denoising applications the white noise assumption fails: structured patterns (e.g. stripes) appear in the images. The model described here addresses cases where the white noise assumption is replaced by a stationary noise as- sumption. An application to an emerging fluorescence microscopy technique (SPIM: Selective Plane Illumination Microscope) is presented, where the adequate noise modeling allows to neatly improve the image quality and provides much better results than methods designed for white noise.

Mots clés: Stripe removal; non linear filtering; fast algo- Stripe removal; fast optimization algorithms; total variation; Gabor filter; SPIM microscope; texture-geometry decompositions; AFM; Tomography

Domaines: Optimisation et contrôle [math.OC]; Organisation et fonctions cellulaires [q-bio.SC]; Analyse numérique [cs.NA]; Optique [physics.optics]

Jérôme Fehrenbach, Corinne Lorenzo, Pierre Weiss. Variational algorithms to remove stationary noise. Application to SPIM imaging.. 2011. ⟨hal-00613097v1⟩