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Microscopy mineral image enhancement based on improved adaptive threshold in nonsubsampled shearlet transform domain

机译:基于改进的Nonsubs采样剪切变换域的显微镜矿物图像增强基于改进的自适应阈值

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摘要

In this paper, a novel microscopy mineral image enhancement method based on adaptive threshold in non-subsampled shearlet transform (NSST) domain is proposed. First, the image is decomposed into one low-frequency sub-band and several high-frequency sub-bands. Second, the gamma correction is applied to process the low-frequency sub-band coefficients, and the improved adaptive threshold is adopted to suppress the noise of the high-frequency sub-bands coefficients. Third, the processed coefficients are reconstructed with the inverse NSST. Finally, the unsharp filter is used to enhance the details of the reconstructed image. Experimental results on various microscopy mineral images demonstrated that the proposed approach has a better enhancement effect in terms of objective metric and subjective metric.
机译:在本文中,提出了一种基于非分配剪切变换(NSST)域的自适应阈值的新型显微镜矿物图像增强方法。首先,图像被分解成一个低频子带和几个高频子带。其次,应用伽马校正来处理低频子带系数,采用改进的自适应阈值来抑制高频子带系数的噪声。第三,用逆NSST重建处理的系数。最后,使用unsharp滤波器用于增强重建图像的细节。各种显微镜矿物图像的实验结果表明,在客观度量和主观度量方面,该方法具有更好的增强效果。

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