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翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Wiener deconvolution ： ウィキペディア英語版 Wiener deconvolution In mathematics, Wiener deconvolution is an application of the Wiener filter to the noise problems inherent in deconvolution. It works in the frequency domain, attempting to minimize the impact of deconvolved noise at frequencies which have a poor signal-to-noise ratio. The Wiener deconvolution method has widespread use in image deconvolution applications, as the frequency spectrum of most visual images is fairly well behaved and may be estimated easily. Wiener deconvolution is named after Norbert Wiener. == Definition == Given a system: :$\backslash \; y(t)\; =\; (h$ *x)(t) + n(t) where $$ * denotes convolution and: *$\backslash \; x(t)$ is some input signal (unknown) at time $\backslash \; t$. *$\backslash \; h(t)$ is the known impulse response of a linear time-invariant system *$\backslash \; n(t)$ is some unknown additive noise, independent of $\backslash \; x(t)$ *$\backslash \; y(t)$ is our observed signal Our goal is to find some $\backslash \; g(t)$ so that we can estimate $\backslash \; x(t)$ as follows: :$\backslash \; \backslash hat(t)\; =\; (g$ *y)(t) where $\backslash \; \backslash hat(t)$ is an estimate of $\backslash \; x(t)$ that minimizes the mean square error. The Wiener deconvolution filter provides such a $\backslash \; g(t)$. The filter is most easily described in the frequency domain: :$\backslash \; G(f)\; =\; \backslash frac$ where: * $\backslash \; G(f)$ and $\backslash \; H(f)$ are the Fourier transforms of $\backslash \; g$ and $\backslash \; h$, respectively at frequency $\backslash \; f$. * $\backslash \; S(f)$ is the mean power spectral density of the input signal $\backslash \; x(t)$ * $\backslash \; N(f)$ is the mean power spectral density of the noise $\backslash \; n(t)$ * the superscript $(f)$ and $\backslash \; Y(f)$ are the Fourier transforms of $\backslash hat(t)$ and $y(t)$, respectively) and then performing an inverse Fourier transform on $\backslash \; \backslash hat(f)$ to obtain $\backslash \; \backslash hat(t)$. Note that in the case of images, the arguments $\backslash \; t$ and $\backslash \; f$ above become two-dimensional; however the result is the same. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Wiener deconvolution」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.