IEEE Trans Image Process - Multiplicative noise removal using variable splitting and constrained optimization.

Tópicos

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Resumo

Multiplicative noise (also known as speckle noise) models are central to the study of coherent imaging systems, such as synthetic aperture radar and sonar, and ultrasound and laser imaging. These models introduce two additional layers of difficulties with respect to the standard Gaussian additive noise scenario: (1) the noise is multiplied by (rather than added to) the original image; (2) the noise is not Gaussian, with Rayleigh and Gamma being commonly used densities. These two features of multiplicative noise models preclude the direct application of most state-of-the-art algorithms, which are designed for solving unconstrained optimization problems where the objective has two terms: a quadratic data term (log-likelihood), reflecting the additive and Gaussian nature of the noise, plus a convex (possibly nonsmooth) regularizer (e.g., a total variation or wavelet-based regularizer/prior). In this paper, we address these difficulties by: (1) converting the multiplicative model into an additive one by taking logarithms, as proposed by some other authors; (2) using variable splitting to obtain an equivalent constrained problem; and (3) dealing with this optimization problem using the augmented Lagrangian framework. A set of experiments shows that the proposed method, which we name MIDAL (multiplicative image denoising by augmented Lagrangian), yields state-of-the-art results both in terms of speed and denoising performance.

Resumo Limpo

multipl nois also known speckl nois model central studi coher imag system synthet apertur radar sonar ultrasound laser imag model introduc two addit layer difficulti respect standard gaussian addit nois scenario nois multipli rather ad origin imag nois gaussian rayleigh gamma common use densiti two featur multipl nois model preclud direct applic stateoftheart algorithm design solv unconstrain optim problem object two term quadrat data term loglikelihood reflect addit gaussian natur nois plus convex possibl nonsmooth regular eg total variat waveletbas regularizerprior paper address difficulti convert multipl model addit one take logarithm propos author use variabl split obtain equival constrain problem deal optim problem use augment lagrangian framework set experi show propos method name midal multipl imag denois augment lagrangian yield stateoftheart result term speed denois perform

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