Blind deconvolution for thin-layered confocal imaging

Abstract

1In this paper, we have proposed an Alternate Minimization (AM) algorithm for estimating the Point-Spread Function (PSF) of a Confocal Laser Scanning Microscope (CLSM) and the specimen fluorescence distribution. A 3-D separable Gaussian model is used to restrict the PSF solution space and a constraint on the specimen is used so as to favor the stabilization and convergence of the algorithm. The results obtained from the simulation show that the PSF can be estimated to a high degree of accuracy, and those on real data show better deconvolution as compared to a full theoretical PSF model.

Parametric Blind Deconvolution for Confocal Laser Scanning Microscopy

“…The AM algorithm converged after 40 iterations of the joint MLEMTV and the GD algorithm. The PSF parameters were initialized to 300nm and 600nm for the lateral and the axial case respectively, and the GD algorithm estimated them to be 257.9 and 477.9nm (Pankajakshan, et al [2008a(Pankajakshan, et al [ , 2009b). These are larger (by about 16% and 14.5% for the lateral and the axial case respectively) than their corresponding theoretically calculated values ).…”

“…o is a Markov random field (MRF) on (Ω s ,η), iff o denotes a Gibbs ensemble on Ω s and the energy is a superposition of potentials associated to the cliques (a set of connected pixels). (Dey, et al [2006]; Pankajakshan, et al [ , 2009b), 2 (Conchello & McNally [1996]), 1 -2 (Hom, et al [2007]) norms, or entropy (Verveer, et al [1999]), wavelets (Figueiredo & Nowak [2003]), sparsity (Fergus, et al [2006]) and median root priors (MRP) (Alenius & Ruotsalainen [1997]). These are tabulated in Tab.…”

“…(4.24) becomes the uninformative uniform distribution. Most of the work on this subject is based on this model, and it was also used in Pankajakshan, et al [2009b] as well. On the other hand, when (a g , b g ) = (0,0), Eq.…”

Phase and TV Based Convex Sets for Blind Deconvolution of Microscopic Images

“…It is also possible to use as a the convex cost function and (13) becomes: (14) The solution of (11) can be obtained using the method that we discussed in [23], [36]. The solution is obtained in an iterative manner and the key step in each iteration is an orthogonal projection onto a supporting hyperplane of the set .…”

Estimating hyperparameters and instrument parameters in regularized inversion Illustration forHerschel/SPIRE map making

“…to estimate the instrument A&A 549, A83 (2013) parameters without dedicated observation. The question arises in various fields: optical imaging (Pankajakshani et al 2009), interferometry (Thiébaut 2008), satellite observation (Jalobeanu et al 2002), magnetic resonance force microscopy (Dobigeon et al 2009), fluorescence microscopy (Zhang et al 2007), deconvolution (Orieux et al 2010b), etc. A similar problem deals with non-parametric intrument response (blind inversion), for which the literature is also very abundant: (Mugnier et al 2004;Thiébaut & Conan 1995;Fusco et al 1999;Conan et al 1998) in astronomy and (Lam & Goodman 2000;Likas & Galatsanos 2004;Molina et al 2006;Bishop et al 2008;Xu & Lam 2009) in the signal-image literature represent examples.…”