本文關(guān)鍵詞： 小波分解 維納濾波 閾值估計(jì) 圖像降噪 峰值信噪比　出處：《組合機(jī)床與自動(dòng)化加工技術(shù)》2017年11期 　論文類型：期刊論文

【摘要】：針對(duì)傳統(tǒng)Bayes閾值不能隨小波分解尺度變換以及提高傳統(tǒng)算法圖像降噪效果的問(wèn)題,文章提出一種改進(jìn)的基于小波維納濾波與Bayes自適應(yīng)閾值估計(jì)圖像降噪算法,該算法在多層小波變換的基礎(chǔ)上,對(duì)小波分解后的第一層細(xì)節(jié)系數(shù)進(jìn)行維納濾波處理,對(duì)其他層細(xì)節(jié)系數(shù)進(jìn)行改進(jìn)Bayes軟閾值估計(jì)算法處理,最后對(duì)處理后的小波系數(shù)進(jìn)行重構(gòu),得到降噪圖像。實(shí)驗(yàn)結(jié)果表明,該方法在圖像峰值信噪比(PSNR)定量指標(biāo)上優(yōu)于傳統(tǒng)的小波Bayes軟閾值估計(jì)圖像降噪方法,并將該方法成功的應(yīng)用于軸承缺陷圖像的降噪預(yù)處理以及軸承缺陷圖像邊緣檢測(cè)中,達(dá)到了圖像降噪的優(yōu)化效果。
[Abstract]:Aiming at the problem that the traditional Bayes threshold can not be transformed with the wavelet decomposition scale transform and improves the image denoising effect of the traditional algorithm, an improved image denoising algorithm based on wavelet Wiener filter and Bayes adaptive threshold estimation is proposed in this paper. On the basis of multi-layer wavelet transform, the first detail coefficient of wavelet decomposition is processed by Wiener filter, the other layer detail coefficients are processed by improved Bayes soft threshold estimation algorithm, and the wavelet coefficients are reconstructed. The experimental results show that the proposed method is superior to the traditional wavelet Bayes soft threshold estimation method in image denoising. The method is successfully applied to the pre-processing of bearing defect image and the edge detection of bearing defect image, and the optimal effect of image de-noising is achieved.
【作者單位】： 北京信息科技大學(xué)現(xiàn)代測(cè)控技術(shù)教育部重點(diǎn)實(shí)驗(yàn)室;
【基金】：國(guó)家高技術(shù)研究發(fā)展計(jì)劃(863計(jì)劃)(2015AA043702) 北京市教委科技計(jì)劃重點(diǎn)項(xiàng)目(KZ201611232032);北京市教委科研計(jì)劃項(xiàng)目(KM201611232020)
【分類號(hào)】：TH133.3;TP391.41

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