基于改進(jìn)的自適應(yīng)差分演化算法的二維Otsu多閾值圖像分割

羅鈞; 楊永松; 侍寶玉

doi:10.11999/JEIT180949

基于改進(jìn)的自適應(yīng)差分演化算法的二維Otsu多閾值圖像分割

doi: 10.11999/JEIT180949 cstr: 32379.14.JEIT180949

重慶大學(xué)光電技術(shù)及系統(tǒng)教育部重點(diǎn)實(shí)驗(yàn)室??重慶??400030

詳細(xì)信息

作者簡(jiǎn)介:
羅鈞：男，1963年生，教授，博士生導(dǎo)師，研究方向?yàn)槟Ｊ阶R(shí)別與人工智能，精密機(jī)械及測(cè)試計(jì)量，智能信息處理

楊永松：男，1994年生，碩士生，研究方向?yàn)榍度胧较到y(tǒng)，機(jī)器視覺

侍寶玉：女，1994年生，碩士生，研究方向?yàn)榍度胧较到y(tǒng)，機(jī)器視覺

通訊作者:
羅鈞　luojun@cqu.edu.cn

中圖分類號(hào): TP391.41
計(jì)量
- 文章訪問數(shù): 3806
- HTML全文瀏覽量: 1482
- PDF下載量: 185
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2018-10-12
- 修回日期: 2019-03-04
- 網(wǎng)絡(luò)出版日期: 2019-03-28
- 刊出日期: 2019-08-01

Multi-threshold Image Segmentation of 2D Otsu Based on Improved Adaptive Differential Evolution Algorithm

Key Laboratory of Optoelectronic Technology and System of Ministry of Education, Chongqing University, Chongqing 400030, China

摘要

摘要: 針對(duì)常規(guī)最大類間方差法在多閾值圖像分割中存在的運(yùn)算量大、計(jì)算時(shí)間長(zhǎng)、分割精度較低等問題，該文提出一種基于改進(jìn)的自適應(yīng)差分演化(JADE)算法的2維Otsu多閾值分割法。首先，為增強(qiáng)初始化種群的質(zhì)量、提升控制參數(shù)的適應(yīng)性，將混沌映射機(jī)制融入到JADE算法中；進(jìn)而，通過該改進(jìn)算法求解2維 Otsu 多閾值圖像的最佳分割閾值；最終，將該算法與差分進(jìn)化(DE), JADE，改進(jìn)正弦參數(shù)自適應(yīng)的差分進(jìn)化(LSHADE-cnEpSin)以及增強(qiáng)的適應(yīng)性微分變換差分進(jìn)化(EFADE) 4種算法的2維Otsu多閾值圖像分割進(jìn)行比較。實(shí)驗(yàn)結(jié)果表明，與其它4種算法相比，基于改進(jìn)JADE算法的2維Otsu多閾值圖像分割在分割速度以及精度上均有較明顯的改善。
- 圖像分割 /
- 最大類間方差法 /
- 混沌映射 /
- 改進(jìn)的自適應(yīng)差分演化算法
Abstract: The multi-threshold image segmentation of the classical 2D maximal between-cluster variance method has deficiencies such as large computation, long calculation time, low segmentation precision and so on. A multi-threshold segmentation of 2D Otsu based on improved Adaptive Differential Evolution (JADE) algorithm is proposed. Firstly, in order to enhance the quality of the initialized population and improve the adaptability of the control parameters, the chaotic mapping mechanism is integrated into the JADE algorithm. Furthermore, the optimal segmentation threshold of 2D Otsu multi-threshold image is solved by improved JADE algorithm. Finally, the algorithm is compared with multi-threshold image segmentation method of 2D Otsu based on Differential Evolution (DE), JADE, Improved Differential Evolution with Adaptive Sinusoidal Parameters (LSHADE-cnEpSin) and Enhanced Adaptive Differential Transformation Differential Evolution (EFADE) algorithm. The experimental results show that compared with the other four algorithms, the multi-threshold image segmentation of 2D Otsu based on the improved JADE algorithm has a significant improvement in terms of segmentation speed and accuracy.
- Image segmentation /
- Maximum interclass variance method /
- Chaotic map /
- Improved Adaptive Differential Evolution (JADE) algorithm

HTML全文

圖 1 2維多閾值分割直方圖

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圖 2 基于CJADE算法2維Otsu多閾值分割方法流程圖

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圖 3 分割效果圖

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圖 4 進(jìn)化曲線圖

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表 1 算法1：混沌映射更新參數(shù)u_F和u_CR的偽代碼

　(1) If $\;\alpha < \beta $

　(2) 　　${u_{\rm CR}} = {u_1} \cdot {u_{\rm CR}} \cdot (1 - {u_{\rm CR}})$

　(3) 　　${u_F} = {u_2} \cdot {u_F} \cdot (1 - {u_F})$

　(4) Else

　(5) 　　${u_{\rm CR}} = (1 - c) \cdot {u_{\rm CR}} + c \cdot {{\rm mean}_{\rm A}}({S_{\rm CR}})$

　(6) 　　${u_F} = (1 - c) \cdot {u_F} + c \cdot {{\rm mean}_{\rm L}}({S_F})$

　(7) End If

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表 2 PSNR、運(yùn)算時(shí)間以及迭代次數(shù)的對(duì)比

算法		Lena (512$ \times $512)			Finger (256$ \times $256)			Pepper (512$ \times $512)
算法		2閾值	3閾值	4閾值	2閾值	3閾值	4閾值	2閾值	3閾值	4閾值
DE算法	PSNR(dB)	10.58	13.88	15.64	12.02	12.45	14.14	11.68	15.84	16.54
	收斂時(shí)間(s)	7.79	7.82	7.84	3.64	3.58	3.73	8.49	8.34	8.82
	迭代次數(shù)	72	58	64	62	57	66	45	43	47
JADE算法	PSNR(dB)	11.79	14.25	16.02	12.35	13.02	14.26	11.71	16.32	16.71
	收斂時(shí)間(s)	0.85	0.83	0.77	0.51	0.53	0.57	0.81	0.80	0.83
	迭代次數(shù)	52	54	50	59	62	58	60	56	58
LSHADE-cnEpSin算法	PSNR(dB)	13.70	14.98	15.67	12.07	12.77	14.46	12.23	16.19	17.02
	收斂時(shí)間(s)	0.79	0.75	0.82	0.45	0.48	0.46	0.78	0.82	0.78
	迭代次數(shù)	34	35	33	65	45	60	50	48	46
EFADE算法	PSNR(dB)	12.89	15.05	15.45	13.23	12.61	13.24	12.11	15.57	16.67
	收斂時(shí)間(s)	0.99	1.12	1.10	0.77	0.76	0.83	1.24	1.31	1.29
	迭代次數(shù)	45	42	46	50	48	52	40	38	41
CJADE算法	PSNR(dB)	13.93	15.64	16.25	13.65	14.67	14.89	12.56	16.57	17.12
	收斂時(shí)間(s)	0.64	0.66	0.65	0.45	0.44	0.48	0.61	0.64	0.66
	迭代次數(shù)	38	35	38	41	40	44	40	36	38

下載: 導(dǎo)出CSV

表 3 閾值和距離測(cè)度值的對(duì)比

算法		Lena (512$ \times $512)			Finger (256$ \times $256)			Pepper (512$ \times $512)
算法		2閾值	3閾值	4閾值	2閾值	3閾值	4閾值	2閾值	3閾值	4閾值
DE算法	距離測(cè)度	4645.67	4698.86	4747.74	1223.45	1247.75	1296.25	5340.87	5407.71	5513.28
DE算法	閾值	(68,71) (117,153)	(30, 32) (86,138) (193,199)	(88,95) (119,123) (151,153) (202,207)	(39,53) (155,165)	(108,124) (147,152) (168,180)	(23,38) (102,133) (150,157) (169,170)	(70,70) (117,161)	(84, 85) (142,162) (201,203)	(70,77) (111,112) (126,129) (129,179)
JADE算法	距離測(cè)度	4842.77	4912.21	4924.13	1315.43	1320.35	1326.23	5798.46	5822.86	5892.86
JADE算法	閾值	(89,149) (193,195)	(77,79) (114,149) (196,196)	(70,77) (109,137) (149,154) (182,183)	(138,166) (175,175)	(10,67) (143,164) (174,175)	(40,52) (50,110) (156,156) (171,172)	(88,91) (127,169)	(98, 115) (140,140) (178,178)	(96,101) (114,133) (149,150) (152,171)
LSHADE-cnEpSin算法	距離測(cè)度	4862.49	4905.97	4995.04	1256.65	1268.79	1289.32	5797.85	5899.34	5909.58
LSHADE-cnEpSin算法	閾值	(88,149) (194,195)	(79,79) (115,145) (177,177)	(76,76) (119,141) (158,160) (197,197)	(88,102) (183,183)	(64,82) (148,164) (183,184)	(36,39) (42,98) (145,155) (164,169)	(78,79) (126,177)	(84, 85) (127,159) (194,194)	(76,77) (121,122) (126,157) (192,193)
EFADE算法	距離測(cè)度	4848.87	4951.82	4973.23	1257.29	1267.42	1324.41	5788.61	5885.72	5892.13
EFADE算法	閾值	(89,148) (186,186)	(76,80) (130,153) (205,205)	(79,86) (112,137) (139,151) (201,203)	(44,51) (142,176)	(30,42) (140,162) (172,174)	(41,46) (68,83) (141,167) (172,173)	(86,90) (120,176)	(73, 74) (121,159) (193,194)	(53,55) (121,123) (154,154) (180,182)
CJADE算法	距離測(cè)度	4863.53	4977.34	4999.63	1327.84	1329.17	1331.28	5799.13	5898.73	5912.18
CJADE算法	閾值	(87,149) (194,194)	(77,78) (115,148) (194,195)	(78,80) (117,139) (156,156) (199,199)	(143,166) (173,173)	(25, 62) (142,166) (174,175)	(40,45) (70,98) (156,158) (162,162)	(84,85) (124,173)	(77, 78) (123,164) (195,195)	(54,55) (99,100) (129,160) (199,199)

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