[1]化春键,张爱榕,陈莹.基于改进的Retinex算法的草坪杂草识别[J].江苏农业学报,2021,(06):1417-1424.[doi:doi:10.3969/j.issn.1000-4440.2021.05.008]
　HUA Chun-jian,ZHANG Ai-rong,CHEN Ying.Lawn weed recognition based on improved Retinex algorithm[J].,2021,(06):1417-1424.[doi:doi:10.3969/j.issn.1000-4440.2021.05.008]
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基于改进的Retinex算法的草坪杂草识别()
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江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2021年06期
页码:
1417-1424
栏目:
植物保护
出版日期:
2021-12-30

文章信息/Info

Title:
Lawn weed recognition based on improved Retinex algorithm
作者:
化春键1; 2; 张爱榕1; 2; 陈莹3
(1.江南大学机械工程学院，江苏无锡214122；2.江苏省食品先进制造装备技术重点实验室，江苏无锡214122；3.江南大学物联网工程学院，江苏无锡214122)
Author(s):
HUA Chun-jian1; 2; ZHANG Ai-rong1; 2; CHEN Ying2
(1.School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China;2.Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment & Technology, Wuxi 214122, China;3.School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China)
关键词:
图像增强; 局部方差; 局部密度; Retinex算法; 草坪杂草
Keywords:
image enhancement; local variance; local density; Retinex algorithm; lawn weed
分类号:
TP391.41;S451.1
DOI:
doi:10.3969/j.issn.1000-4440.2021.05.008
文献标志码:
A
摘要:
针对草坪杂草图像前景与背景灰度相近导致图像前景难以识别的问题，本研究提出一种基于局部密度的Retinex增强算法。首先，为了突出图像前景，平滑杂乱背景，利用局部方差对图像进行预处理。其次，为了更准确地得到所需部分像素的空间信息，利用多阈值分割和开运算差分将像素分为前景、背景和待细分像素3类，利用局部密度提取待细分像素的空间信息。最后，为了融合局部密度信息，采用Sigmoid函数优化反射分量灰度变换系数，得到增强图像。结果表明，本研究算法增强效果良好，能有效扩大杂草与草坪草的灰度差，抑制背景噪声，峰值信噪比相对传统Retinex算法提高24.23%。
Abstract:
Aiming at the problem of indiscernible foreground of lawn weed images caused by the similarity of gray level between image background and image foreground, a Retinex enhancement algorithm based on local density was proposed. Firstly, to highlight the foreground and smooth the background clutter of the images, local variance was used to preprocess the images. Secondly, to obtain the spatial information of the required part of the pixels more accurately, the pixels were divided into three kinds, such as foreground pixels, background pixels and pixels to be subdivided by using multi threshold segmentation and open operation difference. The spatial information of the pixels to be subdivided was extracted by local density method. Finally, to incorporate the local density information, Sigmoid function was used to optimize the gray level transformation coefficient of reflection component to obtain the enhanced images. The results showed that, the proposed algorithm had good enhancement effect, which could expand the gray level difference effectively between weeds and lawn grasses, and could suppress background noise. The peak signal-to-noise ratio by this method was 24.23% higher compared with the traditional Retinex algorithm.

参考文献/References:

[1]曾永贤,金晨钟,向国红,等. 娄底市草坪杂草种类、分布及危害[J]. 草业科学，2017, 34(11): 2272-2278.
[2]杭楠,王翔宇,张蕴薇,等. 结缕草草坪杂草化学防除策略[J]. 草业科学, 2019, 36(9): 2259-2269.
[3]KHAIRUZZAMAN A K M, CHAUDHURY S. Masi entropy based multilevel thresholding for image segmentation[J]. Multimedia Tools and Applications, 2019, 78(23): 33573-33591.
[4]MOHEBBIFAR M R, OMARMELI K. Defect detection by combination of threshold and multistep watershed techniques[J]. Russian Journal of Nondestructive Testing, 2020, 56(1): 80-91.
[5]KHAN M F, REN X, KHAN E. Semi dynamic fuzzy histogram equalization[J]. Optik, 2015, 126(21): 2848-2853.
[6]NARDECCHIA A, VITALE R, DUPONCHEL L. Fusing spectral and spatial information with 2-D stationary wavelet transform (SWT 2-D) for a deeper exploration of spectroscopic images[J]. Talanta, 2021, 224: 121835.
[7]LI M, LIU J, YANG W, et al. Structure-revealing low-light image enhancement via robust Retinex model[J]. IEEE Transactions on Image Processing, 2018, 27(6): 2828-2841.
[8]PARK S, YU S, MOON B, et al. Low-light image enhancement using variational optimization-based retinex model[J]. IEEE Transactions on Consumer Electronics, 2017, 63(2): 178-184.
[9]TENG L, XUE F, BAI Q. Remote sensing image enhancement via edge-preserving multi-scale Retinex[J]. IEEE Photonics Journal, 2019, 11(2): 1-10.
[10]赵宏宇,肖创柏,禹晶,等. 马尔科夫随机场模型下的Retinex夜间彩色图像增强[J]. 光学精密工程, 2014, 22(4): 1048-1055.
[11]赵鹏,蒋宇中,翟琦,等. 基于局部方差中值滤波的极/超低频信道噪声抑制方法[J]. 电子学报, 2019, 47(4): 955-961.
[12]RODRIGUEZ A, LAIO A. Clustering by fast search and find of density peaks[J]. Science, 2014, 344(6191): 1492-1496.
[13]吴禄慎,程伟,胡赟. 应用改进布谷鸟算法优化多阈值图像分割[J]. 吉林大学学报(工学版), 2021, 51(1): 358-369.
[14]白元明，孔令成，张志华，等. 基于改进OSTU算法的快速作物图像分割［J］．江苏农业科学，2019,47(24):231-236.
[15]代振,王平波,卫红凯. 非高斯背景下基于Sigmoid函数的信号检测[J]. 电子与信息学报, 2019, 41(12): 2945-2950.
[16]林昌,周海峰,陈武. 基于双边滤波的高斯金字塔变换Retinex图像增强算法[J]. 激光与光电子学进展, 2020, 57(16): 209-215.
[17]汤新雨,李敏,徐灵丽,等. 基于透射率修正的湍流模型与动态调整Retinex的水下图像增强[J]. 中国图象图形学报, 2020, 25(7): 1380-1392.

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备注/Memo

备注/Memo:
收稿日期：2021-04-27基金项目：国家自然科学基金项目(61573168)作者简介：化春键(1975-)，男，北京人,博士，副教授,主要从事图形图像处理、计算机视觉等方面的研究。（E-mail）cjhua@jiangnan.edu.cn
更新日期/Last Update: 2022-01-07