Remote sensing image denoising based on NSCT and PCA

Computer Engineering and Applications ›› 2011, Vol. 47 ›› Issue (30): 195-197.

• 图形、图像、模式识别 • Previous Articles Next Articles

Remote sensing image denoising based on NSCT and PCA

WANG Yalan1，JIA Zhenhong1，YANG Jie2，PANG Shaoning3

1.College of Information Science and Engineering，Xinjiang University，Urumqi 830046，China
2.Institute of Image Processing and Pattern Recognition，Shanghai Jiaotong University，Shanghai 200240，China
3.Knowledge Engineering and Discovery Research Institute，Auckland University of Technology，Auckland 1020，New Zealand

Received:1900-01-01 Revised:1900-01-01 Online:2011-10-21 Published:2011-10-21

基于NSCT域主分量分析的遥感图像去噪方法

汪雅兰1，贾振红1，杨杰2，庞韶宁3

1.新疆大学信息科学与工程学院，乌鲁木齐 830046
2.上海交通大学图像处理与模式识别研究所，上海 200240
3.新西兰奥克兰理工大学知识工程与开发研究所，新西兰奥克兰 1020

Abstract

Abstract: A new local adaptive threshold estimation method for image denoising based on the Nonsubsampled Contourlet Transform（NSCT） and the Principal Component Analysis（PCA） is proposed in this paper.By utilizing noise energy based on PCA in NSCT domain，a local adaptive threshold is proposed which considering the clustering property of the coefficients for remote sensing image denoising.The simulation experimental results show that the proposed method can effectively reduce Gauss noise in remote sensing image and preserve image edg.Compared with the contourlet hard-thresholding，PCA in contourlet domain and the NSCT hard-thresholding denoising method，the proposed method is obviously superior both in vision and in PSNR.

Key words: image denoising, Nonsubsmapled Contourlet Transform（NSCT）, Principal Component Analysis（PCA）, adaptive threshold

摘要： 提出一种新的结合非下采样Contourlet变换（NSCT）和主分量分析（PCA）的图像自适应阈值去噪方法。通过PCA估计NSCT域中的噪声能量，并与NSCT系数的领域信息相结合，构造出自适应阈值对遥感图像进行去噪。仿真实验结果表明，提出的方法与Contourlet硬阈值，基于Contourlet的图像PCA和NSCT硬阈值去噪方法相比能够有效去除遥感图像的高斯噪声，较完整地保持图像的边缘等细节信息，提高了图像的峰值信噪比，图像视觉效果也有明显改善。

关键词: 图像去噪, 非下采样Contourlet（NSCT）, 主分量分析, 自适应阈值

WANG Yalan1，JIA Zhenhong1，YANG Jie2，PANG Shaoning3. Remote sensing image denoising based on NSCT and PCA[J]. Computer Engineering and Applications, 2011, 47(30): 195-197.

汪雅兰1，贾振红1，杨杰2，庞韶宁3. 基于NSCT域主分量分析的遥感图像去噪方法[J]. 计算机工程与应用, 2011, 47(30): 195-197.

[1]	LIU Di, JIA Jinlu, ZHAO Yuqing, QIAN Yurong. Overview of Image Denoising Methods Based on Deep Learning [J]. Computer Engineering and Applications, 2021, 57(7): 1-13.
[2]	CHEN Renhe, LAI Zhenyi, QIAN Yurong. Improved Image Denoising Generative Adversarial Network Algorithm [J]. Computer Engineering and Applications, 2021, 57(5): 168-172.
[3]	YU Duo, HUANG Yongdong. Hyperspectral Image Classification Based on SPCA and Domain Transform Recursive Filtering [J]. Computer Engineering and Applications, 2021, 57(4): 199-208.
[4]	QI Xiaoxiang, LI Min, ZHU Ying, SONG Yu, DU Weidong. Adaptive Region Segmentation of SAR Image Based on Edge Detection [J]. Computer Engineering and Applications, 2021, 57(22): 232-240.
[5]	WANG Jie, JIN Zhengmeng, FENG Can. Adaptive Generalized Total Variation Algorithm for Poisson Noise Removal [J]. Computer Engineering and Applications, 2021, 57(20): 203-209.
[6]	HU Yaping, KONG Weiwei, LI Meng, HUANG Cuiling. Improved Panoramic Image Mosaic Algorithm for TV Image Denoising Model [J]. Computer Engineering and Applications, 2021, 57(17): 203-209.
[7]	CHEN Xiaowen, LIU Guangshuai, LIU Wanghua, LI Xurui. Pairwise Rotation-Invariant Co-occurrence Adaptive Complete Local Ternary Pattern [J]. Computer Engineering and Applications, 2021, 57(1): 219-226.
[8]	LIN Kezheng, ZHANG Yuanming, LI Haotian. Research on HOG Feature Extraction Algorithm Weighted by Information Entropy [J]. Computer Engineering and Applications, 2020, 56(6): 147-152.
[9]	HUANG Guangjun, DENG Yuanlong. Polarizer Visual Defect Detection and Classification Based on Improved LBP and SVM Algorithm [J]. Computer Engineering and Applications, 2020, 56(22): 251-255.
[10]	LIU Chengshi, ZHAO Zhigang, LI Qiang, LV Huixian, DONG Xiaochen, LI Jinxia. Enhanced Low-Rank Representation Image Denoising Algorithm [J]. Computer Engineering and Applications, 2020, 56(2): 216-225.
[11]	YUAN Xiaojun, ZHOU Tao, LI Chen. Research on Image Denoising Algorithm Based on Non-local Clustering with Sparse Prior [J]. Computer Engineering and Applications, 2020, 56(18): 177-185.
[12]	YANG Yongpeng, YANG Zhenzhen, LI Jianlin, LE Jun. Low Rank and Sparse Decomposition and Its Application in Video and Image Processing [J]. Computer Engineering and Applications, 2020, 56(16): 21-30.
[13]	WANGChundan, XIE Hongwei, LI Yaxuan, ZHANG Hao. Motion Object Detection with Improved Three-Frame Difference and ViBe Algorithm [J]. Computer Engineering and Applications, 2020, 56(13): 199-203.
[14]	CHEN Jia, LIU Dongxue, WU Dashuo. Stock Index Forecasting Method Based on Feature Selection and LSTM Model [J]. Computer Engineering and Applications, 2019, 55(6): 108-112.
[15]	YANG-ZHAO Qilin, PENG Dingtao, TANG Qi, LUO Xiaomin. Smoothing Quasi-Newton Algorithm for [lp] Regularization of Sparse Optimization [J]. Computer Engineering and Applications, 2019, 55(22): 163-171.

Remote sensing image denoising based on NSCT and PCA

基于NSCT域主分量分析的遥感图像去噪方法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics