Luisier, FlorianFlorianLuisierTHIERRY BLUWolfe, Patrick J.Patrick J.Wolfe2024-03-072024-03-072012-07-2710577149https://scholars.lib.ntu.edu.tw/handle/123456789/64052630 double-spaced pages, 11 figures; submitted for publicationIn this paper, we derive an unbiased expression for the expected mean-squared error associated with continuously differentiable estimators of the noncentrality parameter of a chi-square random variable. We then consider the task of denoising squared-magnitude magnetic resonance (MR) image data, which are well modeled as independent noncentral chi-square random variables on two degrees of freedom. We consider two broad classes of linearly parameterized shrinkage estimators that can be optimized using our risk estimate, one in the general context of undecimated filterbank transforms, and the other in the specific case of the unnormalized Haar wavelet transform. The resultant algorithms are computationally tractable and improve upon most state-of-the-art methods for both simulated and actual MR image data. © 1992-2012 IEEE.enChi-square distribution | filterbank transform | image denoising | magnetic resonance (MR) imaging | Rician noise | unbiased risk estimation; Statistics - Applications; Statistics - Applications; Statistics - Methodologyjournal article10.1109/TIP.2012.2191565224910822-s2.0-84864151725https://api.elsevier.com/content/abstract/scopus_id/84864151725