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基于回歸分析和主成分分析的噪聲方差估計方法

吳疆 尤飛 蔣平

吳疆, 尤飛, 蔣平. 基于回歸分析和主成分分析的噪聲方差估計方法[J]. 電子與信息學(xué)報, 2018, 40(5): 1195-1201. doi: 10.11999/JEIT170624
引用本文: 吳疆, 尤飛, 蔣平. 基于回歸分析和主成分分析的噪聲方差估計方法[J]. 電子與信息學(xué)報, 2018, 40(5): 1195-1201. doi: 10.11999/JEIT170624
WU Jiang, YOU Fei, JIANG Ping. Noise Variance Estimation Method Based on Regression Analysis and Principal Component Analysis[J]. Journal of Electronics & Information Technology, 2018, 40(5): 1195-1201. doi: 10.11999/JEIT170624
Citation: WU Jiang, YOU Fei, JIANG Ping. Noise Variance Estimation Method Based on Regression Analysis and Principal Component Analysis[J]. Journal of Electronics & Information Technology, 2018, 40(5): 1195-1201. doi: 10.11999/JEIT170624

基于回歸分析和主成分分析的噪聲方差估計方法

doi: 10.11999/JEIT170624
基金項(xiàng)目: 

國家自然科學(xué)基金(11641002),榆林市科技計劃項(xiàng)目(Gy13-12),陜西省教育廳科研項(xiàng)目(11JK0636)

Noise Variance Estimation Method Based on Regression Analysis and Principal Component Analysis

Funds: 

The National Natural Science Foundation of China (11641002), The Science and Technology Program of Yulin (Gy13-12), The Program of Education Commission of Shaanxi Province (11JK0636)

  • 摘要: 準(zhǔn)確可靠的噪聲強(qiáng)度估計是數(shù)字圖像處理領(lǐng)域中一個重要的研究課題。噪聲估計的難點(diǎn)在于如何提取用于估計的純噪聲信息,近幾年,許多算法采用主成分分析技術(shù)來避免圖像紋理信息的干擾,用最小特征值來估計噪聲方差,可以有效地減少圖像紋理信息對估計結(jié)果的影響,所以這類方法對于高頻圖像(豐富紋理圖像)效果很好。由于圖像塊數(shù)量有限,最小特征值實(shí)際上比真實(shí)噪聲方差小,而且圖像塊數(shù)量越少,偏差越大。如果直接把最小特征值作為估計方差,則容易低估計高噪聲。該文通過回歸分析確定最小特征值跟真實(shí)噪聲方差的比值和圖像塊數(shù)量呈冪函數(shù)關(guān)系,因此可以通過最小特征值和冪函數(shù)關(guān)系得到真實(shí)的噪聲方差。實(shí)驗(yàn)表明該文方法既能處理高頻圖像,又適合各種噪聲水平,同時也能處理乘性高斯噪聲。
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出版歷程
  • 收稿日期:  2017-06-28
  • 修回日期:  2017-11-24
  • 刊出日期:  2018-05-19

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