基于contourlet域尺度间系数相关性的图像去噪

Fan Yang, Ruizhen Zhao, Shaohai Hu
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引用次数: 1

摘要

提出了一种基于contourlet变换的图像去噪算法。该方法考虑了收缩过程中尺度间轮廓线系数的相关性,并假设无噪声轮廓线系数与其位于不同尺度的母系数相关。通过计算跨尺度的相关系数,我们认为较小的系数更有可能是噪声系数。然后我们去掉那些大小和相关系数都很小的系数。实验结果表明,该算法既能保持图像的边缘,又能获得较高的PSNR和较好的视觉质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Image denoising based on correlations of inter-scale coefficients in contourlet domain
A new image denoising algorithm based on contourlet transform is presented in this paper. The new approach takes the correlations of inter-scale contourlet coefficients into account in the process of shrinkage, and assumes that the noise-free contourlet coefficients are correlated to their parent coefficients which locate at a different scale. By computing the relativity coefficients across scales, we consider those with smaller values are more likely the noisy coefficients. And then we remove those coefficients whose magnitudes and corresponding relativity coefficients are both small. Experimental results demonstrate that the proposed algorithm can not only maintain the edges of an image, but obtain higher PSNR and better visual quality.
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