Improved extreme value weighted sparse representational image denoising with random perturbation
Improved extreme value weighted sparse representational image denoising with random perturbation
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DOI：
10.1117/1.jei.24.6.063004
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发表时间：
2015-11
影响因子：
1.1
通讯作者：
Shibin Xuan;Yulan Han
中科院分区：
文献类型：
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作者：
Shibin Xuan;Yulan Han
抽象的。混合噪声的去除是图像去噪领域的研究热点。目前，加权编码与稀疏非局部正则化是一个很好的混合噪声去除方法。为了使拟合函数更接近稳健估计技术的要求，使用了极值技术，使拟合函数在更大的区间上满足稳健估计的三个条件。此外，在去噪模型中引入随机扰动序列，以防止迭代求解过程陷入局部最优。在图像去噪模型的迭代过程中，采用基于Radon变换的噪声检测算法和自适应中值滤波器获得高质量的初始解。实验结果表明，该方法有效地增强了稀疏非局部正则化模型的加权编码。该方法能有效地去除图像中的混合噪声，同时较好地保留了图像的边缘和细节。
Abstract. Research into the removal of mixed noise is a hot topic in the field of image denoising. Currently, weighted encoding with sparse nonlocal regularization represents an excellent mixed noise removal method. To make the fitting function closer to the requirements of a robust estimation technique, an extreme value technique is used that allows the fitting function to satisfy three conditions of robust estimation on a larger interval. Moreover, a random disturbance sequence is integrated into the denoising model to prevent the iterative solving process from falling into local optima. A radon transform-based noise detection algorithm and an adaptive median filter are used to obtain a high-quality initial solution for the iterative procedure of the image denoising model. Experimental results indicate that this improved method efficiently enhances the weighted encoding with a sparse nonlocal regularization model. The proposed method can effectively remove mixed noise from corrupted images, while better preserving the edges and details of the processed image.