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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 Bilateral filter ： ウィキペディア英語版 Bilateral filter A bilateral filter is a non-linear, edge-preserving and noise-reducing smoothing filter for images. The intensity value at each pixel in an image is replaced by a weighted average of intensity values from nearby pixels. This weight can be based on a Gaussian distribution. Crucially, the weights depend not only on Euclidean distance of pixels, but also on the radiometric differences (e.g. range differences, such as color intensity, depth distance, etc.). This preserves sharp edges by systematically looping through each pixel and adjusting weights to the adjacent pixels accordingly. The bilateral filter is defined as $$ I^\text(x) = \frac \sum_ I(x_i)f_r(\|I(x_i)-I(x)\|)g_s(\|x_i-x\|), where the normalization term $$ W_p = \sum_ ensures that the filter preserves image energy and * $I^\backslash text$ is the filtered image; * $I$ is the original input image to be filtered; * $x$ are the coordinates of the current pixel to be filtered; * $\backslash Omega$ is the window centered in $x$; * $f\_r$ is the range kernel for smoothing differences in intensities. This function can be a Gaussian function; * $g\_s$ is the spatial kernel for smoothing differences in coordinates. This function can be a Gaussian function; As mentioned above, the weight $W\_p$ is assigned using the spatial closeness and the intensity difference.〔Carlo Tomasi and Roberto Manduchi, “Bilateral filtering for gray and color images,” in Computer Vision, 1998. Sixth International Conference on . IEEE, 1998, pp. 839– 846.〕 Consider a pixel located at $(i,\; j)$ which needs to be denoised in image using its neighbouring pixels and one of its neighbouring pixels is located at $(k,\; l)$. Then, the weight assigned for pixel $(k,\; l)$ to denoise the pixel $(i,\; j)$ is given by: $$ w(i, j, k, l)= e^- \frac)} where σd and σr are smoothing parameters and I(i, j) and I(k, l) are the intensity of pixels $(i,\; j)$ and $(k,\; l)$ respectively. After calculating the weights, normalize them. $$ I_D(i, j)=\frac } } where $I\_D$ is the denoised intensity of pixel $(i,\; j)$. == Parameters== * As the range parameter σr increases, the bilateral filter gradually approaches Gaussian convolution more closely because the range Gaussian widens and flattens, which means that it becomes nearly constant over the intensity interval of the image. * As the spatial parameter σd increases, the larger features get smoothened. 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「Bilateral filter」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.