matlab畢業(yè)設(shè)計外文翻譯--復(fù)雜脊波圖像去噪-文庫吧
2025-04-16 12:58 本頁面
【正文】 這種情況下取得較好的效果。在某些情況下， ComRidgeletShrink能夠比普通 RidgeletShrink多提供給我們 。這表明，我們把二元樹結(jié)合復(fù)數(shù)的小波變換成脊波變換能夠明顯的改善我們圖像去噪的效果。 ComRidgeletShrink超越 VisuShrink的表現(xiàn)更重要的是所有噪音水平和圖像測試。圖一顯示的是在無噪 音圖像 ，添 加噪音 的圖 像，用 VisuShrink去噪 的圖 像，用RidgeletShrink去噪的圖像，用 ComRidgeletShrink去噪的圖像，用 wiener2去噪的圖像，在一個分區(qū)大小為 32*32的象素塊中。 ComRidgeletShrink在視覺上產(chǎn)生的效果比 VisuShrink ， wiener2 RidgeletShrink更清晰具有高線性和恢復(fù)曲線的特點。 4．結(jié)論和未來工作 在這篇文章中我們研究了 用復(fù)雜脊波對圖像去噪。復(fù)雜脊波變換是通過執(zhí)行一維二元樹復(fù)雜小波在空氣中氡的變換系數(shù)獲得的。氡變換是通過投影片定理得到的。對于圖像去噪近似轉(zhuǎn)換不變性質(zhì)的二元樹復(fù)數(shù)小波變換對于復(fù)雜小波變換是一個很好的選擇。復(fù)雜脊波變換提供了近乎完美的對于光滑的物體和表現(xiàn)對象與邊緣。這使噪音閾值的脊波系數(shù)更接近高斯白噪音的消噪。我們測試了我們新的去噪方法和幾個標(biāo)準(zhǔn)圖像和加入高斯白噪音的圖像。用一個非常簡單的硬閾值復(fù)雜脊波系數(shù)。在這些脊波實驗中，實驗結(jié)果表明復(fù)雜的脊波有更好的去噪能力比起 VisuShrink和普通的 wiener2。 我們建議ComRidgeletShrink用于實際的圖像去噪中。未來工作主要是考慮在復(fù)雜圖像應(yīng)用曲波復(fù)雜脊波。同樣，復(fù)雜脊波還可以應(yīng)用的不變特征提取模式識別方法。 Complex Ridgelets for Image Denoising G. Y. Chen and B. Kegl 1 Introduction Wavelet transforms have been successfully used in many scientific fields such as image pression, image denoising, signal processing, puter graphics,and pattern recognition, to name only a and his coworkers pioneered a wavelet denoising scheme by using soft thresholding and hard thresholding. This approach appears to be a good choice for a number of applications. This is because a wavelet transform can pact the energy of the image to only a small number of large coefficients and the majority of the wavelet coeficients are very small so that they can be set to zero. The thresholding of the wavelet coeficients can be done at only the detail wavelet deposition subbands. We keep a few low frequency wavelet subbands untouched so that they are not thresholded. It is well known that Donoho39。s method offers the advantages of smoothness and adaptation. However, as Coifman and Donoho pointed out, this algorithm exhibits visual artifacts: Gibbs phenomena in the neighbourhood of discontinuities. Therefore, they propose in a translation invariant (TI) denoising scheme to suppress such artifacts by averaging over the denoised signals of all circular shifts. The experimental results in confirm that single TI wavelet denoising performs better than the nonTI case. Bui and Chen extended this TI scheme to the multiwavelet case and they found that TI multiwavelet denoising gave better results than TI single wavelet denoising. Cai and Silverman proposed a thresholding scheme by taking the neighbour coeficients into account Their experimental results showed apparent advantages over the traditional termbyterm wavelet and Bui extended this neighbouring wavelet thresholding idea to the multiwavelet case. They claimed that neighbour multiwavelet denoising outperforms neighbour single wavelet denoising for some standard test signals and reallife et al. proposed an image denoising scheme by considering a square neighbourhood in the wavelet domain. Chen et al. also tried to customize the wavelet _lter and the threshold for image denoising. Experimental results show that these two methods produce better denoising results. The ridgelet transform was developed over several years to break the limitations of the wavelet transform. The 2D wavelet transform of images produces large wavelet coeficients at every scale of the so many large coe_cients, the denoising of noisy images faces a lot of diff
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