IEEE Trans Image Process - Sparse Poisson noisy image deblurring.

Tópicos

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Resumo

Deblurring noisy Poisson images has recently been a subject of an increasing amount of works in many areas such as astronomy and biological imaging. In this paper, we focus on confocal microscopy, which is a very popular technique for 3-D imaging of biological living specimens that gives images with a very good resolution (several hundreds of nanometers), although degraded by both blur and Poisson noise. Deconvolution methods have been proposed to reduce these degradations, and in this paper, we focus on techniques that promote the introduction of an explicit prior on the solution. One difficulty of these techniques is to set the value of the parameter, which weights the tradeoff between the data term and the regularizing term. Only few works have been devoted to the research of an automatic selection of this regularizing parameter when considering Poisson noise; therefore, it is often set manually such that it gives the best visual results. We present here two recent methods to estimate this regularizing parameter, and we first propose an improvement of these estimators, which takes advantage of confocal images. Following these estimators, we secondly propose to express the problem of the deconvolution of Poisson noisy images as the minimization of a new constrained problem. The proposed constrained formulation is well suited to this application domain since it is directly expressed using the antilog likelihood of the Poisson distribution and therefore does not require any approximation. We show how to solve the unconstrained and constrained problems using the recent alternating-direction technique, and we present results on synthetic and real data using well-known priors, such as total variation and wavelet transforms. Among these wavelet transforms, we specially focus on the dual-tree complex wavelet transform and on the dictionary composed of curvelets and an undecimated wavelet transform.

Resumo Limpo

deblur noisi poisson imag recent subject increas amount work mani area astronomi biolog imag paper focus confoc microscopi popular techniqu d imag biolog live specimen give imag good resolut sever hundr nanomet although degrad blur poisson nois deconvolut method propos reduc degrad paper focus techniqu promot introduct explicit prior solut one difficulti techniqu set valu paramet weight tradeoff data term regular term work devot research automat select regular paramet consid poisson nois therefor often set manual give best visual result present two recent method estim regular paramet first propos improv estim take advantag confoc imag follow estim second propos express problem deconvolut poisson noisi imag minim new constrain problem propos constrain formul well suit applic domain sinc direct express use antilog likelihood poisson distribut therefor requir approxim show solv unconstrain constrain problem use recent alternatingdirect techniqu present result synthet real data use wellknown prior total variat wavelet transform among wavelet transform special focus dualtre complex wavelet transform dictionari compos curvelet undecim wavelet transform

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