| 光谱学与光谱分析 |
|
|
|
|
|
| Wavelet NeighShrink Method for Grid Texture Removal in Image of Solar Radio Bursts |
| ZHAO Rui-zhen1,HU Zhan-yi2 |
1. School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
2. National Key Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China |
|
|
|
|
Abstract The data received from solar bursts contain a lot of noise, which makes further processing more difficult. To remove the noise and enhance the image, we studied the properties of the NeighShrink threshold function and analyzed the influence of neighborhood window size on the denoising result, on the basis of which a new wavelet NeighShrink square root method for image denoising is presented. Firstly, each channel of the solar burst image is normalized, which can, to some extent, remove the horizontal grid texture in the image. Secondly, the preprocessed image is decomposed by wavelet transform, and the obtained wavelet coefficients are thresholded by NeighShrink square root method. Finally, the denoised image is reconstructed by inverse wavelet transform. The experimental results show that the presented method is effective in noise removal and image enhancement.
|
|
Received: 2005-10-19
Accepted: 2006-03-11
|
|
|
|
Corresponding Authors:
ZHAO Rui-zhen
E-mail: rzhzhao@bjtu.edu.cn
|
|
| Cite this article: |
|
ZHAO Rui-zhen,HU Zhan-yi. Wavelet NeighShrink Method for Grid Texture Removal in Image of Solar Radio Bursts [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2007, 27(01): 198-201.
|
|
|
|
| URL: |
|
https://www.gpxygpfx.com/EN/Y2007/V27/I01/198 |
[1] NING Zong-jun, FU Qi-jun，LU Quan-kang(宁宗军, 傅其骏, 陆全康). Acta Astrophysics Sinica(天体物理学报), 2000, 20(1): 101.
[2] CHEN Pu-chun, ZHANG Xi-zhen, XIANG Shou-ping，et al(陈普春, 张喜镇, 向守平，等). Acta Astronomica Sinica(天文学报), 2000, 41(3): 327.
[3] ZHAO Rui-zhen, HU Zhan-yi，ZHAO Yong-heng(赵瑞珍, 胡占义, 赵永恒). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2005, 25(1): 153.
[4] Faghhih F，Smith M. IEEE Trans. on Image Processing, 2002, 11(9): 1062.
[5] Daubechies I. Ten lectures on wavelets. Philadelphia: Society for Industrial and Applied Mathematics, 1992.
[6] Donoho D L. IEEE Trans. on Information Theory, 1995, 41(3): 613.
[7] Cai T T，Silverman B W. The Indian Journal of Statistics, 2001, 63(B): 127.
[8] Chen G Y, Bui T D，Krzyzak A. International Conference on Acoustics, Speech, and Signal Processing, 2004, Ⅱ: 917. |
| [1] |
LI He1, WANG Yu2, FAN Kai2, MAO Yi-lin2, DING Shi-bo3, SONG Da-peng3, WANG Meng-qi3, DING Zhao-tang1*. Evaluation of Freezing Injury Degree of Tea Plant Based on Deep
Learning, Wavelet Transform and Visible Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 234-240. |
| [2] |
ZHOU Bei-bei1, LI Heng-kai1*, LONG Bei-ping2. Variation Analysis of Spectral Characteristics of Reclaimed Vegetation in an Ionic Rare Earth Mining Area[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3946-3954. |
| [3] |
DU Zhi-heng1, 2, 3, HE Jian-feng1, 2, 3*, LI Wei-dong1, 2, 3, WANG Xue-yuan1, 2, 3, YE Zhi-xiang1, 2, 3, WANG Wen1, 2, 3. A New EDXRF Spectral Decomposition Method for Sharpening Error Wavelets[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1719-1724. |
| [4] |
LI Wei1, 2, HE Yao1, 2, LIN Dong-yue2, DONG Rong-lu2*, YANG Liang-bao2*. Remove Background Peak of Substrate From SERS Signals of Hair Based on Gaussian Mixture Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 854-860. |
| [5] |
WANG Ren-jie1, 2, FENG Peng1*, YANG Xing3, AN Le3, HUANG Pan1, LUO Yan1, HE Peng1, TANG Bin1, 2*. A Denoising Algorithm for Ultraviolet-Visible Spectrum Based on
CEEMDAN and Dual-Tree Complex Wavelet Transform[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 976-983. |
| [6] |
WANG Fan1, 2, CHEN Long-yue2, 3, DUAN Dan-dan1, 2, 4*, CAO Qiong1, 4, ZHAO Yu1, LAN Wan-rong5. Estimation of Total Nitrogen Content in Fresh Tea Leaves Based on
Wavelet Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3235-3242. |
| [7] |
YU Xin, ZHOU Wei*, XIE Dong-cai, XIAO Feng, LI Xin-yu. The Study of Digital Baseline Estimation in CVAFS[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(08): 2392-2396. |
| [8] |
JIANG Rong-chang1, 2, GU Ming-sheng2, ZHAO Qing-he1, LI Xin-ran1, SHEN Jing-xin1, 3, SU Zhong-bin1*. Identification of Pesticide Residue Types in Chinese Cabbage Based on Hyperspectral and Convolutional Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1385-1392. |
| [9] |
LI Xue-ying1, 2, LI Zong-min3*, CHEN Guang-yuan4, QIU Hui-min2, HOU Guang-li2, FAN Ping-ping2*. Prediction of Tidal Flat Sediment Moisture Content Based on Wavelet Transform[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1156-1161. |
| [10] |
Yumiti Maiming1, WANG Xue-mei1, 2*. Hyperspectral Estimation of Soil Organic Matter Content Based on Continuous Wavelet Transformation[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1278-1284. |
| [11] |
CHEN Feng-nong1, SANG Jia-mao1, YAO Rui1, SUN Hong-wei1, ZHANG Yao1, ZHANG Jing-cheng1, HUANG Yun2, XU Jun-feng3. Rapid Nondestructive Detection and Spectral Characteristics Analysis of Factors Affecting the Quality of Dendrobium Officinale[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 3276-3280. |
| [12] |
CHEN Jian-hong, LIN Zhi-qiang, SUN Chao-yue. Determination of New Non-Invasive Blood Glucose Detection Method Based on Spectral Decomposition[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(08): 2378-2383. |
| [13] |
WU Lian-hui1, 2, 3, HE Jian-feng1, 2, 3*, ZHOU Shi-rong2, 3, WANG Xue-yuan1, 2, YE Zhi-xiang2, 3. A Multi-Derivation-Spline Wavelet Analysis Method for Low Atomic Number Element EDXRF[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(08): 2530-2535. |
| [14] |
YANG Bao-hua1, GAO Yuan1, WANG Meng-xuan1, QI Lin1, NING Jing-ming2. Estimation Model of Polyphenols Content in Yellow Tea Based on Spectral-Spatial Features[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(03): 936-942. |
| [15] |
ZHENG Hai-ming, ZHU Xiao-peng, FENG Shuai-shuai, JIA Gui-hong. Experimental Research on Monitoring of BTX Concentration Based on Differential Optical Absorption Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(02): 467-472. |
|
|
|
|
