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Image Denoising Based on Dilated Singularity Prior

机译：基于膨胀奇异先验的图像去噪

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摘要

In order to preserve singularities in denoising, we propose a new scheme by adding dilated singularity prior to noisy images. The singularities are detected by canny operator firstly and then dilated using mathematical morphology for finding pixels "near" singularities instead of "on" singularities. The denoising results for pixels near singularities are obtained by nonlocal means in spatial domain to preserve singularities while the denoising results for pixels in smooth regions are obtained by EM algorithm constrained by a mask formed by downsampled spatial image with dilated singularity prior to suiting the sizes of the subbands of wavelets. The final denoised results are got by combining the above two results. Experimental results show that the scheme can preserve singularity well with relatively high PSNR and good visual quality.
机译：为了在去噪中保持奇异性，我们提出了一种通过在噪声图像之前添加膨胀奇异性的新方案。首先由canny运算符检测奇异点，然后使用数学形态学对其进行扩张，以查找“近”奇点而不是“ on”奇点的像素。通过在空间域中通过非局部方法获得接近奇异点的像素去噪结果，以保留奇异性；而在平滑区域像素的去噪结果通过EM算法获得，并通过对模板进行下采样，该掩模由具有奇异点的降采样后的空间图像形成，并适合于小波的子带。最终的去噪结果是通过结合以上两个结果而得到的。实验结果表明，该方案能够以较高的PSNR和良好的视觉质量很好地保持奇异性。

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  • 来源
    《Mathematical Problems in Engineering》 |2012年第5期|p.25.1-25.15|共15页
  • 作者

    Shaoxiang Hu; Zhiwu Liao; Wufan Chen;

  • 作者单位

    School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China;

    Visual Computing and Virtual Reality Key Laboratory of Sichuan Province, Sichuan Normal University, Chengdu 610066, China;

    School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China;

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