A CURE for Noisy Magnetic Resonance Images: Chi-Square Unbiased Risk Estimation
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摘要In 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.
著者Luisier F, Blu T, Wolfe PJ
期刊名稱IEEE Transactions on Image Processing
出版年份2012
月份8
日期1
卷號21
期次8
出版社Institute of Electrical and Electronics Engineers (IEEE)
頁次3454 - 3466
國際標準期刊號1057-7149
電子國際標準期刊號1941-0042
語言英式英語
關鍵詞Chi-square distribution; filterbank transform; image denoising; magnetic resonance (MR) imaging; Rician noise; unbiased risk estimation
Web of Science 學科類別Computer Science; Computer Science, Artificial Intelligence; COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE; Engineering; Engineering, Electrical & Electronic; ENGINEERING, ELECTRICAL & ELECTRONIC

上次更新時間 2020-17-11 於 23:40