Signal-Preserving Erratic Noise Attenuation via Iterative Robust Sparsity-Promoting Filter
Signal-Preserving Erratic Noise Attenuation via Iterative Robust Sparsity-Promoting Filter
复制标题
通过迭代鲁棒稀疏性促进滤波器保持信号不稳定的噪声衰减
DOI：
10.1109/tgrs.2018.2802462
复制
发表时间：
2018-06
影响因子：
8.2
通讯作者：
Chen Yangkang
中科院分区：
文献类型：
--
作者：
Zhao Qiang;Du Qizhen;Gong Xufei;Chen Yangkang
在高斯分布假设下，稀疏域阈值滤波器在稀疏域中对高斯随机噪声有很好的去除效果。不稳定的噪声，它指定的非高斯噪声，包括已知或未知分布的大型孤立事件，也需要明确考虑。然而，基于最小二乘（LS）准则的常规稀疏域阈值滤波器对具有高振幅和非高斯噪声的数据严重敏感，即，不稳定的噪声，这使得抑制这种类型的噪声极具挑战性。本文提出了一种鲁棒的稀疏促进去噪模型，在该模型中，用Huber准则代替LS准则，以削弱不稳定噪声的影响。该方法通过引入一个数据自适应参数来区分随机噪声和不稳定噪声，其中随机噪声用均方来描述，而不稳定噪声则通过阻尼权进行降权。与传统的稀疏域阈值滤波器不同，通过Huber准则定义噪声数据和恢复信号之间的失配导致非线性优化问题。借助理论拟地震数据，提出了一种迭代鲁棒稀疏提升滤波器，通过迭代过程将非线性优化问题转化为线性LS问题。这种变换的主要优点是非线性去噪滤波器可以通过传统的LS求解器来求解。实验结果表明，与传统的基于LS准则的去噪方法相比，该去噪滤波器能够有效地滤除不稳定噪声，且不损伤有用信号。
Sparse domain thresholding filters operating in a sparse domain are highly effective in removing Gaussian random noise under Gaussian distribution assumption. Erratic noise, which designates non-Gaussian noise that consists of large isolated events with known or unknown distribution, also needs to be explicitly taken into account. However, conventional sparse domain thresholding filters based on the least-squares (LS) criterion are severely sensitive to data with high-amplitude and non-Gaussian noise, i.e., the erratic noise, which makes the suppression of this type of noise extremely challenging. In this paper, we present a robust sparsity-promoting denoising model, in which the LS criterion is replaced by the Huber criterion to weaken the effects of erratic noise. The random and erratic noise is distinguished by using a data-adaptive parameter in the presented method, where random noise is described by mean square, while the erratic noise is downweighted through a damped weight. Different from conventional sparse domain thresholding filters, definition of the misfit between noisy data and recovered signal via the Huber criterion results in a nonlinear optimization problem. With the help of theoretical pseudoseismic data, an iterative robust sparsity-promoting filter is proposed to transform the nonlinear optimization problem into a linear LS problem through an iterative procedure. The main advantage of this transformation is that the nonlinear denoising filter can be solved by conventional LS solvers. Tests with several data sets demonstrate that the proposed denoising filter can successfully attenuate the erratic noise without damaging useful signal when compared with conventional denoising approaches based on the LS criterion.
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DOI：
10.1017/cbo9780511794308
发表时间：
2012
期刊：
--
影响因子：
--
作者：
Gitta Kutyniok
通讯作者：
Gitta Kutyniok
DOI：
10.3997/2214-4609.201400112
发表时间：
2009-06
期刊：
--
影响因子：
--
作者：
M. Schonewille;A. Vigner;Alan Ryder
通讯作者：
M. Schonewille;A. Vigner;Alan Ryder
影响因子：
--
作者：
通讯作者：
--
影响因子：
3.3
作者：
Mousavi, S. Mostafa;Langston, Charles A.
通讯作者：
Langston, Charles A.
影响因子：
3.3
作者：
Mousavi, S. Mostafa;Langston, Charles A.;Horton, Stephen P.
通讯作者：
Horton, Stephen P.