基于曲波变换与拟合优度检验的遥感图像去噪方法

doi:10.13229/j.cnki.jdxbgxb.20211455

Abstract

Abstract:

To solve the denoising problem from visible light remote sensing image， a denoising method for remote sensing image based on Curvelet transformation and Goodness of Fit test is proposed. The method first decomposes the remote sensing image by Curvelet theory to get the decomposition coefficients. Then the method normalizes Curvelet coefficients， and tests the normalized Curvelet coefficients locally by using the goodness of fit test. The real signal coefficients are obtained after the goodness of fit test， and the coefficients are inversely normalized to obtain the inversely normalized Curvelet coefficients. Finally， Curvelet coefficients are inversely transformed to obtain the denoised remote sensing image. The denoising algorithm is compared with Wavelet threshold denoising algorithm， Curvelet threshold denoising algorithm， Discrete wavelet transform and Goodness of Fit test denoising algorithm and Curvelet cyclic translation denoising algorithm. Experimental results show that this algorithm is better than the above algorithms in the indexes of peak signal-to-noise ratio and structural similarity.

Key words: computational mathematics, visible light remote sensing image, Curvelet transform, Goodness of Fit test, image denoising

CLC Number:

TP751

Li-bo CHENG,Xin-yue LI,Zhe LI,Xiao-ning JIA. Remote sensing image denoising method based on curvelet transform and goodness-of-fit test[J].Journal of Jilin University(Engineering and Technology Edition), 2023, 53(11): 3207-3213.

Figures/Tables 7

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Table 1

Evaluation index results of denoising effect using different algorithms for remote sensing images"

图像	算法	$σ = 10$		$σ = 15$		$σ = 20$		$σ = 25$
图像	算法	PSNR/dB	SSIM	PSNR/dB	SSIM	PSNR/dB	SSIM	PSNR/dB	SSIM
ImageA	小波阈值去噪	28.51	0.751	26.02	0.645	24.20	0.554	22.78	0.478
	曲波阈值去噪	29.08	0.806	26.72	0.755	25.24	0.717	24.35	0.688
	GOF-DWT去噪	28.99	0.816	27.02	0.773	25.66	0.739	24.56	0.685
	曲波循环平移去噪	29.80	0.827	27.70	0.781	26.37	0.748	25.48	0.722
	GOF-Curvelet去噪	31.12	0.835	28.73	0.785	27.15	0.733	25.95	0.685
ImageB	小波阈值去噪	28.27	0.750	25.94	0.651	24.21	0.564	22.84	0.491
	曲波阈值去噪	29.03	0.804	26.86	0.755	25.42	0.716	24.52	0.687
	GOF-DWT去噪	28.53	0.789	26.75	0.746	25.47	0.710	24.45	0.678
	曲波循环平移去噪	29.79	0.821	27.80	0.774	26.52	0.741	25.66	0.713
	GOF-Curvelet去噪	30.67	0.835	28.64	0.783	27.19	0.734	25.89	0.682
ImageC	小波阈值去噪	26.27	0.740	24.69	0.662	23.51	0.593	22.48	0.530
	曲波阈值去噪	27.31	0.774	25.43	0.693	24.32	0.631	23.61	0.583
	GOF-DWT去噪	26.86	0.744	25.42	0.672	24.30	0.607	23.61	0.562
	曲波循环平移去噪	27.98	0.803	26.04	0.718	24.90	0.652	24.10	0.601
	GOF-Curvelet去噪	28.49	0.806	26.90	0.750	25.64	0.695	24.70	0.648
ImageD	小波阈值去噪	26.72	0.773	24.54	0.679	22.99	0.597	21.75	0.524
	曲波阈值去噪	27.63	0.804	24.95	0.735	23.31	0.678	22.24	0.631
	GOF-DWT去噪	28.20	0.820	26.15	0.771	24.83	0.731	23.66	0.690
	曲波循环平移去噪	28.46	0.821	26.05	0.764	24.55	0.719	23.56	0.683
	GOF-Curvelet去噪	29.40	0.833	27.24	0.784	25.74	0.738	24.54	0.690

Table 1

References 29

1	Gao J. Editorial for the special issue "ecosystem services with remote sensing"[J]. Remote Sensing, 2020, 12(14): 12142191.
2	Cao D G, Xing H F, Wong M S, et al. A stacking ensemble deep learning model for building extraction from remote sensing images[J]. Remote Sensing, 2021, 13(19): 13193898.
3	唐尧, 王立娟, 马国超, 等. 利用国产遥感卫星进行金沙江高位滑坡灾害灾情应急监测[J]. 遥感学报, 2019, 23(2): 252-261.
	Tang Yao, Wang Li-juan, Ma Guo-chao, et al. Emergency monitoring of high-level landslide disasters in Jinsha River using domestic remote sensing satellites[J]. Journal of Remote Sensing, 2019, 23(2): 252-261.
4	周培诚, 程塨, 姚西文, 等. 高分辨率遥感影像解译中的机器学习范式[J]. 遥感学报, 2021, 25(1): 182-197.
	Zhou Pei-cheng, Cheng Gong, Yao Xi-wen, et al. Machine learning paradigms in high-resolution remote sensing image interpretation[J]. National Remote Sensing Bulletin, 2021, 25(1): 182-197.
5	Marston J M, D'Alpoim Guedes J, Warinner C. Method and Theory in Paleoethnobotany[M]. Denver: University Press of Colorado, 2014.
6	Donoho D L. De-noising by soft-thresholding[J]. IEEE Transactions on Information Theory, 1995, 41(3): 613-627.
7	Cai T T, Silverman B W. Incorporating information on neighbouring coefficients into wavelet estimation[J]. The Indian Journal of Statistics, Series B, 2001, 63(2): 127-148.
8	Chen G Y, Bui T D, Krzyżak A. Image denoising with neighbour dependency and customized wavelet and threshold[J]. Pattern Recognition, 2005, 38(1): 115-124.
9	Zhou D W, Cheng W G. Image denoising with an optimal threshold and neighbouring window[J]. Pattern Recognition Letters, 2008, 29(11): 1694-1697.
10	杨恢先, 王绪四, 谢鹏鹤, 等. 改进阈值与尺度间相关的小波红外图像去噪[J]. 自动化学报, 2011, 37(10): 1167-1174.
	Yang Hui-Xian, Wang Xu-si, Xie Peng-he, et al. Infrared image denoising based on improved threshold and inter-scale correlations of wavelet transform[J]. Acta Automatica Sinica, 2011, 37(10): 1167-1174.
11	Candes E, Demanet L, Donoho D, et al. Fast discrete curvelet transform[J]. SIAM Journal on Multiscale Modeling and Simulation, 2006, 5(3): 861-899.
12	Rahman S M M, Ahmad M O, Swamy M N S. Bayesian wavelet-based image denoising using the Gauss-Hermite expansion[J]. IEEE Transactions on Image Processing, 2008, 17(10): 1755-1771.
13	Sinthuja S, Saravanan S. GIS based satellite image de-noising using curvelet transform[J]. Indonesian Journal of Electrical Engineering and Computer Science, 2017, 8(3): 654- 656.
14	Wu C, Ma X, Wang W. Hyperspectral image denoise based on curvelet transform combined with weight coefficient method[J]. Journal of Intelligent & Fuzzy Systems, 2019, 37(4): 4425-4429.
15	Raju C, Reddy T S, Sivasubramanyam M. Denoising of remotely sensed images via curvelet transform and its relative assessment[J]. Procedia Computer Science, 2016, 89: 771-777.
16	李雄飞, 宋璐, 张小利. 基于协同经验小波变换的遥感图像融合[J]. 吉林大学学报: 工学版, 2019, 49(4): 1307-1319.
	Li Xiong-fei, Song Lu, Zhang Xiao-Li. Remote sensing image fusion based on cooperative empirical wavelet transform[J]. Journal of Jilin University (Engineering and Technology Edition), 2019, 49(4): 1307-1319.
17	刘迪, 贾金露, 赵玉卿, 等. 基于深度学习的图像去噪方法研究综述[J]. 计算机工程与应用, 2021, 57(7): 1-13.
	LIU Di, Jia Jin-lu, Zhao Yu-Qing, et al. Overview of image denoising methods based on depth learning[J]. Computer Engineering and Applications, 2021, 57(7): 1-13.
18	Isola P, Zhu J Y, Zhou T, et al. Image to image translation with conditional adversarial networks[C]∥ IEEE Conference on Computer Vision and Pattern Recognition, Hawaii, USA, 2017: 1125-1134.
19	Zeng J, Pang J, Sun W, et al. Deep graph Laplacian regularization for robust denoising of real images[C]∥ IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, USA, 2019: 1759-1768.
20	王智文, 李绍滋. 基于多元统计模型的分形小波自适应图像去噪[J]. 计算机学报, 2014, 37(6): 1380-1389.
	Wang Zhi-wen, Li Shao-zi. Adaptive fractal-wavelet image denoising base on multivariate statistical model[J]. Chinese Journal of Computers, 2014, 37(6): 1380-1389.
21	Cui Y Q, Wang K. Image denoising based on local statistical models in wavelet domain[J]. Opto-Electronic Engineering, 2007, 34(3): 93-97.
22	Bacchelli S, Papi S. Statistically based multiwavelet de-noising[J]. Journal of Computational and Applied Mathematics, 2007, 210(1/2): 47-55.
23	赵海英, 张小利, 李雄飞, 等. 基于多尺度Meanshift图像去噪算法[J]. 吉林大学学报: 工学版, 2014, 44(5): 1417-1422.
	Zhao Hai-ying, Zhang Xiao-li, Li Xiong-fei, et al. Image denoising algorithm based on multi-scale meanshift[J]. Journal of Jilin University (Engineering and Technology Edition), 2014, 44(5): 1417-1422.
24	Tian J, Chen L, Ma L H. A wavelet-domain non-parametric statistical approach for image denoising[J]. IEICE Electronics Express, 2010, 7(18): 1409-1415.
25	Boubchir L, Boashash B. Wavelet denoising based on the MAP estimation using the BKF Prior with application to images and EEG signals[J]. IEEE Transactions on Signal Processing, 2013, 61(8): 1880-1894.
26	Naveed K, Shaukat B, Ehsan S, et al. Multiscale image denoising using Goodness-of-Fit test based on EDF statistics[J]. PLoS ONE, 2019, 14(5): 0216197.
27	Naveed K, Rehman N U. Wavelet based multivariate signal denoising using Mahalanobis distance and EDF statistics[J]. IEEE Transactions on Signal Processing, 2020, 68: 5997-6010.
28	Anderson T W, Darling D A. A test of goodness of fit[J]. Journal of the American Statistical Association, 1954, 268(49): 765-769.
29	Donoho D L, Johnstone J M. Ideal spatial adaptation by wavelet shrinkage[J]. Biometrika, 1994, 81(3): 425-455.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 10

[1]	LI Shoutao, LI Yuanchun. Autonomous Mobile Robot Control Algorithm Based on Hierarchical Fuzzy Behaviors in Unknown Environments[J]. 吉林大学学报(工学版), 2005, 35(04): 391 -397 .
[2]	Liu Qing-min，Wang Long-shan，Chen Xiang-wei，Li Guo-fa. Ball nut detection by machine vision[J]. 吉林大学学报(工学版), 2006, 36(04): 534 -538 .
[3]	Li Hong-ying; Shi Wei-guang;Gan Shu-cai. Electromagnetic properties and microwave absorbing property of Z type hexaferrite Ba_3-xLa_xCo₂Fe₂₄O₄₁[J]. 吉林大学学报(工学版), 2006, 36(06): 856 -0860 .
[4]	Zhang Quan-fa，Li Ming-zhe，Sun Gang，Ge Xin . Comparison between flexible and rigid blank-holding in multi-point forming[J]. 吉林大学学报(工学版), 2007, 37(01): 25 -30 .
[5]	Yang Shu-kai, Song Chuan-xue, An Xiao-juan, Cai Zhang-lin . Analyzing effects of suspension bushing elasticity on vehicle yaw response character with virtual prototype method[J]. 吉林大学学报(工学版), 2007, 37(05): 994 -0999 .
[6]	. [J]. 吉林大学学报(工学版), 2007, 37(06): 1284 -1287 .
[7]	Che Xiang-jiu，Liu Da-you，Wang Zheng-xuan . Construction of joining surface with G¹ continuity for two NURBS surfaces[J]. 吉林大学学报(工学版), 2007, 37(04): 838 -841 .
[8]	Liu Han-bing, Jiao Yu-ling, Liang Chun-yu,Qin Wei-jun . Effect of shape function on computing precision in meshless methods[J]. 吉林大学学报(工学版), 2007, 37(03): 715 -0720 .
[9]	. [J]. 吉林大学学报(工学版), 2007, 37(04): 0 .
[10]	Li Yue-ying，Liu Yong-bing，Chen Hua . Surface hardening and tribological properties of a cam materials[J]. 吉林大学学报(工学版), 2007, 37(05): 1064 -1068 .

Remote sensing image denoising method based on curvelet transform and goodness-of-fit test

RICH HTML

PDF (PC)

Abstract

Cite this article

share this article

Figures/Tables 7

References 29

Related Articles 6

Metrics

Comments

Recommended 10

[1]	Zhi-dan CAI,Ming FANG,Zhe LI,Jia-lu XU. Blind remote sensing image deblurring algorithm based on Gaussian curvature and reweighted graph total variation [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(9): 2649-2658.
[2]	WU Kai-jun, WANG Chun-li, SHAN Ya-zhou, DU San-shan, LU Huai-wei. Chemical synapse coupling synchronization of Hindmarsh-Rose neurons under Gauss white noise [J]. 吉林大学学报(工学版), 2017, 47(5): 1554-1560.
[3]	JIANG Chao, GENG Ze-xun, LIU Li-yong, PAN Ying-feng. Maximum likelihood image restoration combined with image denoising [J]. 吉林大学学报(工学版), 2015, 45(4): 1360-1366.
[4]	ZHANG Tian, SUN Yan-kui, TIAN Xiao-lin. Optical coherence tomography image denoising method by merging dyadic wavelet and anisotropic diffusion filter [J]. 吉林大学学报(工学版), 2013, 43(增刊1): 340-344.
[5]	Zhang Qiang，Guo Bao-long . Image fusion algorithm using Curvelet transform [J]. 吉林大学学报(工学版), 2007, 37(02): 458-0463.
[6]	Cui Yan-qiu；Wang Ke；Sun Wei. Image denoising method based on adaptive Gaussian mixture model in wavelet domain [J]. 吉林大学学报(工学版), 2006, 36(06): 983-0988.