【摘要】：為有效抑制圖像噪聲,提高電氣設(shè)備紅外診斷的準(zhǔn)確性,采用基于小波系數(shù)尺度間相關(guān)性和雙變量收縮函數(shù)的方法對(duì)電纜瓷套終端紅外圖像進(jìn)行去噪.將圖像進(jìn)行小波分解,計(jì)算小波系數(shù)尺度間的相關(guān)系數(shù),使用模糊c-均值聚類(lèi)法對(duì)相關(guān)系數(shù)聚類(lèi),即將小波系數(shù)分為有效系數(shù)和無(wú)效系數(shù)兩類(lèi).對(duì)無(wú)效小波系數(shù)直接進(jìn)行置零處理,對(duì)有效小波系數(shù)使用雙變量收縮函數(shù)進(jìn)行處理,得到真實(shí)圖像小波系數(shù)的估計(jì)值.最后,對(duì)處理得到的真實(shí)圖像小波系數(shù)的估計(jì)值進(jìn)行重構(gòu),便得到去噪后圖像.含噪圖像的去噪結(jié)果表明,運(yùn)用文中方法能有效地去除紅外圖像中的噪聲,且與使用傳統(tǒng)軟閾值方法去噪得到的圖像對(duì)比,文中方法去噪后的圖像信噪比更高,最小均方誤差更小.
[Abstract]:In order to effectively suppress image noise and improve the accuracy of infrared diagnosis of electrical equipment, a method based on wavelet coefficient scale correlation and bivariate shrinkage function is used to de-noise infrared image of cable porcelain cover terminal. The image is decomposed by wavelet, and the correlation coefficients between scales of wavelet coefficients are calculated. The correlation coefficients are clustered by fuzzy c-means clustering method, that is to say, the wavelet coefficients are divided into effective coefficients and invalid coefficients. The invalid wavelet coefficients are directly zeroed, and the effective wavelet coefficients are processed by the bivariate contraction function, and the estimated values of the real image wavelet coefficients are obtained. Finally, the real image is reconstructed from the estimated wavelet coefficients, and the denoised image is obtained. The denoising results of noisy images show that the proposed method can effectively remove the noise in infrared images, and compared with the image denoised by the traditional soft threshold method, the signal-to-noise ratio of the image after denoising is higher. The minimum mean square error is smaller.
【作者單位】： 華南理工大學(xué)電力學(xué)院;珠海供電局;廣州供電局有限公司;
【基金】：國(guó)家高技術(shù)研究發(fā)展計(jì)劃(863計(jì)劃)項(xiàng)目(2015AA050201)~~
【分類(lèi)號(hào)】：TM507;TP391.41

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1 高強(qiáng),趙振兵,李然,俞曉雯;基于獨(dú)立分量分析的近紅外圖像去噪方法的研究與應(yīng)用[J];中國(guó)電機(jī)工程學(xué)報(bào);2005年22期

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本文編號(hào)：2359378