基于改进的自适应差分演化算法的二维Otsu多阈值图像分割

罗钧; 杨永松; 侍宝玉

doi:10.11999/JEIT180949

基于改进的自适应差分演化算法的二维Otsu多阈值图像分割

doi: 10.11999/JEIT180949

重庆大学光电技术及系统教育部重点实验室重庆 400030

详细信息

作者简介:
罗钧：男，1963年生，教授，博士生导师，研究方向为模式识别与人工智能，精密机械及测试计量，智能信息处理

杨永松：男，1994年生，硕士生，研究方向为嵌入式系统，机器视觉

侍宝玉：女，1994年生，硕士生，研究方向为嵌入式系统，机器视觉

通讯作者:
罗钧　luojun@cqu.edu.cn

中图分类号: TP391.41
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出版历程
- 收稿日期: 2018-10-12
- 修回日期: 2019-03-04
- 网络出版日期: 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: 针对常规最大类间方差法在多阈值图像分割中存在的运算量大、计算时间长、分割精度较低等问题，该文提出一种基于改进的自适应差分演化(JADE)算法的2维Otsu多阈值分割法。首先，为增强初始化种群的质量、提升控制参数的适应性，将混沌映射机制融入到JADE算法中；进而，通过该改进算法求解2维 Otsu 多阈值图像的最佳分割阈值；最终，将该算法与差分进化(DE), JADE，改进正弦参数自适应的差分进化(LSHADE-cnEpSin)以及增强的适应性微分变换差分进化(EFADE) 4种算法的2维Otsu多阈值图像分割进行比较。实验结果表明，与其它4种算法相比，基于改进JADE算法的2维Otsu多阈值图像分割在分割速度以及精度上均有较明显的改善。
- 图像分割 /
- 最大类间方差法 /
- 混沌映射 /
- 改进的自适应差分演化算法
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

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图 1 2维多阈值分割直方图

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图 2 基于CJADE算法2维Otsu多阈值分割方法流程图

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图 3 分割效果图

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图 4 进化曲线图

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表 1 算法1：混沌映射更新参数u_F和u_CR的伪代码

　(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、运算时间以及迭代次数的对比

算法		Lena (512$ \times $512)			Finger (256$ \times $256)			Pepper (512$ \times $512)
算法		2阈值	3阈值	4阈值	2阈值	3阈值	4阈值	2阈值	3阈值	4阈值
DE算法	PSNR(dB)	10.58	13.88	15.64	12.02	12.45	14.14	11.68	15.84	16.54
	收敛时间(s)	7.79	7.82	7.84	3.64	3.58	3.73	8.49	8.34	8.82
	迭代次数	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
	收敛时间(s)	0.85	0.83	0.77	0.51	0.53	0.57	0.81	0.80	0.83
	迭代次数	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
	收敛时间(s)	0.79	0.75	0.82	0.45	0.48	0.46	0.78	0.82	0.78
	迭代次数	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
	收敛时间(s)	0.99	1.12	1.10	0.77	0.76	0.83	1.24	1.31	1.29
	迭代次数	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
	收敛时间(s)	0.64	0.66	0.65	0.45	0.44	0.48	0.61	0.64	0.66
	迭代次数	38	35	38	41	40	44	40	36	38

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表 3 阈值和距离测度值的对比

算法		Lena (512$ \times $512)			Finger (256$ \times $256)			Pepper (512$ \times $512)
算法		2阈值	3阈值	4阈值	2阈值	3阈值	4阈值	2阈值	3阈值	4阈值
DE算法	距离测度	4645.67	4698.86	4747.74	1223.45	1247.75	1296.25	5340.87	5407.71	5513.28
DE算法	阈值	(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	距离测度	4842.77	4912.21	4924.13	1315.43	1320.35	1326.23	5798.46	5822.86	5892.86
JADE算法	阈值	(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	距离测度	4862.49	4905.97	4995.04	1256.65	1268.79	1289.32	5797.85	5899.34	5909.58
LSHADE-cnEpSin算法	阈值	(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	距离测度	4848.87	4951.82	4973.23	1257.29	1267.42	1324.41	5788.61	5885.72	5892.13
EFADE算法	阈值	(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	距离测度	4863.53	4977.34	4999.63	1327.84	1329.17	1331.28	5799.13	5898.73	5912.18
CJADE算法	阈值	(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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