本文選題：磁瓦　切入點(diǎn)：非下采樣Shearlet變換　出處：《農(nóng)業(yè)機(jī)械學(xué)報(bào)》2017年03期 　論文類型：期刊論文

【摘要】：針對磁瓦表面裂紋缺陷圖像背景不均勻、對比度低和存在紋理干擾等特點(diǎn),提出了一種基于非下采樣Shearlet變換(Nonsubsampled Shearlet transform,NSST)的裂紋檢測方法。首先對原始圖像進(jìn)行多尺度、多方向NSST分解,得到一個(gè)低頻子帶和多個(gè)高頻子帶,然后利用各向異性擴(kuò)散和改進(jìn)的γ增強(qiáng)方法對高頻子帶進(jìn)行濾波和增強(qiáng);同時(shí)利用二維高斯函數(shù)對低頻子帶進(jìn)行卷積操作來構(gòu)造高斯多尺度空間,估計(jì)出圖像的主要背景,并通過背景差法得到均勻的低頻目標(biāo)圖像。最后通過重構(gòu)NSST系數(shù)得到去噪和增強(qiáng)后的均勻目標(biāo)圖像,利用自適應(yīng)閾值分割和區(qū)域連通法提取裂紋缺陷。實(shí)驗(yàn)結(jié)果表明,所提方法檢測準(zhǔn)確率達(dá)92.5%,優(yōu)于基于形態(tài)學(xué)濾波方法、基于Curvelet變換方法和基于Shearlet變換方法等現(xiàn)有磁瓦表面裂紋檢測方法。
[Abstract]:In view of the characteristics of uneven background, low contrast and texture interference in the surface crack defect image of magnetic tile, a new crack detection method based on non-downsampling Shearlet transform nonsubsampled Shearlet transform (NSST) is proposed. A low frequency subband and a plurality of high frequency subbands are obtained by multidirectional NSST decomposition, and then the high frequency subbands are filtered and enhanced by anisotropic diffusion and improved 緯 enhancement methods. At the same time, we use the two-dimensional Gao Si function to convolution the low-frequency subband to construct the Gao Si multi-scale space, and estimate the main background of the image. The uniform low frequency target image is obtained by background difference method. Finally, the uniform target image after denoising and enhancement is obtained by reconstruction of NSST coefficient, and the crack defects are extracted by adaptive threshold segmentation and region connectivity method. The experimental results show that, The accuracy of the proposed method is 92.5, which is superior to the existing methods such as morphological filtering, Curvelet transform and Shearlet transform.
【作者單位】： 四川大學(xué)制造科學(xué)與工程學(xué)院;昆明理工大學(xué)機(jī)電工程學(xué)院;
【基金】：“十二五”國家科技支撐計(jì)劃項(xiàng)目(2015BAF27B01) 四川省科技支撐計(jì)劃項(xiàng)目(2016GZ0160)
【分類號】：TP391.41;TM351

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本文編號：1632568