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A novel brain image enhancement method based on nonsubsampled contourlet transform

机译:基于非下采样contourlet变换的脑图像增强新方法

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摘要

In this article, a novel brain image enhancement approach based on nonsubsampled contourlet transform (NSCT) is proposed. First, the image is decomposed into a low-frequency component and several high-frequency components by the NSCT; Second, the gamma correction is applied to deal with the low-frequency sub-band coefficients, and the adaptive threshold is used to remove the noise of the high-frequency sub-bands coefficients; Third, the inverse nonsubsampled contourlet transform is adopted to reconstruct the processed coefficients; Finally, the unsharp filter is used to enhance the reconstructed image. The experimental results demonstrate that the performance of the proposed method is superior to the state-of-the-art algorithms in terms of brain image enhancement.
机译:本文提出了一种基于非下采样轮廓波变换(NSCT)的脑图像增强方法。首先,通过NSCT将图像分解为低频分量和几个高频分量。其次,应用伽马校正处理低频子带系数,自适应阈值用于去除高频子带系数的噪声。第三,采用逆非下采样contourlet变换重构处理后的系数。最后,锐化滤波器用于增强重建图像。实验结果表明,在脑图像增强方面,该方法的性能优于最新算法。

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