针对低信噪比情况下地震信号同相轴不易识别的问题,提出用C-WNNM方法来压制地震勘探资料中的随机噪声.给出了基于WNNM的低秩逼近理论,利用地震信号在时间和空间上具有一定的相似性这一特点来构建近似低秩矩阵,并且由CEEMD分解得到的IMF...针对低信噪比情况下地震信号同相轴不易识别的问题,提出用C-WNNM方法来压制地震勘探资料中的随机噪声.给出了基于WNNM的低秩逼近理论,利用地震信号在时间和空间上具有一定的相似性这一特点来构建近似低秩矩阵,并且由CEEMD分解得到的IMF1分量来近似估计局部噪声方差,从而获得更加精确的权值.经过迭代逼近得到最终去噪后的信号.对由雷克子波生成的模拟地震勘探资料进行C-WNNM滤波处理.结果表明,在地震数据存在强噪声的情况下,该方法能够有效压制随机噪声并且能够更好地保留有效信号,信噪比相比于原始WNNM算法提高了3 d B左右.展开更多
As synthetic aperture radar（SAR） has been widely used nearly in every field, SAR image de-noising became a very important research field. A new SAR image de-noising method based on texture strength and weighted nucl...As synthetic aperture radar（SAR） has been widely used nearly in every field, SAR image de-noising became a very important research field. A new SAR image de-noising method based on texture strength and weighted nuclear norm minimization（WNNM） is proposed. To implement blind de-noising, the accurate estimation of noise variance is very important. So far, it is still a challenge to estimate SAR image noise level accurately because of the rich texture. Principal component analysis（PCA） and the low rank patches selected by image texture strength are used to estimate the noise level. With the help of noise level, WNNM can be expected to SAR image de-noising. Experimental results show that the proposed method outperforms many excellent de-noising algorithms such as Bayes least squares-Gaussian scale mixtures（BLS-GSM） method, non-local means（NLM） filtering in terms of both quantitative measure and visual perception quality.展开更多
Terahertz imaging is one of the forefront topics of imaging technology today. Denoising process is the key for improving the resolution of the terahertz holographic reconstructed image. Based on the fact that the weig...Terahertz imaging is one of the forefront topics of imaging technology today. Denoising process is the key for improving the resolution of the terahertz holographic reconstructed image. Based on the fact that the weighted nuclear norm minimization （WNNM） method preserves the details of the reconstructed image well and the non- local mean （NLM） algorithm performs better in the removal of background noise, this paper proposes a new method in which the NLM algorithm is used to improve the WNNM method. The experimental observation and quantitative analysis of the denoising results prove that the new method has better denoising effect for the terahertz holographic reconstructed image.展开更多
文摘针对低信噪比情况下地震信号同相轴不易识别的问题,提出用C-WNNM方法来压制地震勘探资料中的随机噪声.给出了基于WNNM的低秩逼近理论,利用地震信号在时间和空间上具有一定的相似性这一特点来构建近似低秩矩阵,并且由CEEMD分解得到的IMF1分量来近似估计局部噪声方差,从而获得更加精确的权值.经过迭代逼近得到最终去噪后的信号.对由雷克子波生成的模拟地震勘探资料进行C-WNNM滤波处理.结果表明,在地震数据存在强噪声的情况下,该方法能够有效压制随机噪声并且能够更好地保留有效信号,信噪比相比于原始WNNM算法提高了3 d B左右.
基金supported by the National Natural Science Foundation of China(6140130861572063)+7 种基金the Natural Science Foundation of Hebei Province(F2016201142F2016201187)the Natural Social Foundation of Hebei Province(HB15TQ015)the Science Research Project of Hebei Province(QN2016085ZC2016040)the Science and Technology Support Project of Hebei Province(15210409)the Natural Science Foundation of Hebei University(2014-303)the National Comprehensive Ability Promotion Project of Western and Central China
文摘As synthetic aperture radar（SAR） has been widely used nearly in every field, SAR image de-noising became a very important research field. A new SAR image de-noising method based on texture strength and weighted nuclear norm minimization（WNNM） is proposed. To implement blind de-noising, the accurate estimation of noise variance is very important. So far, it is still a challenge to estimate SAR image noise level accurately because of the rich texture. Principal component analysis（PCA） and the low rank patches selected by image texture strength are used to estimate the noise level. With the help of noise level, WNNM can be expected to SAR image de-noising. Experimental results show that the proposed method outperforms many excellent de-noising algorithms such as Bayes least squares-Gaussian scale mixtures（BLS-GSM） method, non-local means（NLM） filtering in terms of both quantitative measure and visual perception quality.
文摘Terahertz imaging is one of the forefront topics of imaging technology today. Denoising process is the key for improving the resolution of the terahertz holographic reconstructed image. Based on the fact that the weighted nuclear norm minimization （WNNM） method preserves the details of the reconstructed image well and the non- local mean （NLM） algorithm performs better in the removal of background noise, this paper proposes a new method in which the NLM algorithm is used to improve the WNNM method. The experimental observation and quantitative analysis of the denoising results prove that the new method has better denoising effect for the terahertz holographic reconstructed image.
