全局稀疏梯度耦合张量扩散的图像去噪模型

发布时间：2018-05-27 06:26

  本文选题：图像去噪 + 全局稀疏梯度　； 参考：《西安电子科技大学学报》2017年06期

【摘要】：针对扩散张量和时滞正则模型产生边缘模糊的问题,提出一种全局稀疏梯度耦合张量扩散的图像去噪模型.首先,该模型利用全局稀疏梯度构造张量矩阵;然后,由张量矩阵引导扩散方程去噪,使其在平滑区域为各项同性扩散以去除噪声,在非平滑区域沿切线方向扩散以保护边缘和细节.算法运用显式Euler差分格式求解提出的模型.数值实验表明,无论是客观度量还是视觉效果,文中提出的方法都能得到较好的去噪结果.实验结果说明,利用全局稀疏梯度能得到更加准确和鲁棒的引导图,从而有效提高了原有方法的去噪效果.
[Abstract]:A global sparse gradient coupled Zhang Liang diffusion image denoising model is proposed to solve the problem of edge blur generated by diffusion Zhang Liang and delay regular model. Firstly, the global sparse gradient is used to construct the Zhang Liang matrix, and then the Zhang Liang matrix is used to guide the diffusion equation to de-noise, so that the diffusion is homogenous in the smooth region to remove the noise. Diffuses along tangent in non-smooth regions to protect edges and details. The algorithm uses explicit Euler difference scheme to solve the proposed model. Numerical experiments show that the proposed method can get better denoising results both in objective measurement and visual effect. The experimental results show that the global sparse gradient can be used to obtain more accurate and robust guide graph, thus effectively improving the denoising effect of the original method.
【作者单位】： 西安电子科技大学数学与统计学院;宁夏大学数学统计学院;
【基金】：国家自然科学基金资助项目(61271294,61472303,61362029,61379030,61472257)
【分类号】：TP391.41
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本文编号：1940837