Ridgelet域中基于非參數自適應密度估計理論的圖像去噪方法
Image De-noising Method Based on Nonparametric Adaptive Density Estimation in Ridgelet Domain
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摘要: Ridgelet 是一種新的信號分析方法，它適合于具有直線或超平面奇異性的二維信號的描述，目前， 針對特定大小的離散圖像，又提出了正交有限Ridgelet變換(FRIT)。該文在有限Ridgelet域中，結合Birge-Massart等提出的非參數自適應估計理論，提出一種新的二維圖像去噪方法。實驗證明，這種基于Ridgelet與Birge-Massart理論的圖像去噪方法，與傳統的Wavelet域去噪以及Donoho閾值去噪方法相比,去噪效果更為明顯。Abstract: Ridgelet is a new signal analysis method; it is especially suitable for describing the 2-D signals which have linear or super-plane singularities. Recently, an orthonormal version of Ridgelet for discrete and finite-size images is presented, named Finite Ridgelet Transform (FRIT). In this paper, a new image de-noising method is proposed by using the threshold method based on nonparametric adaptive estimation which is presented by Birge-Massart in Ridgelet domain. Experiments show that this de-noising method represents better characteristic than traditional de-noising method in wavelet domain and the de-noising method based on Donoho strategy.
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