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Position: Home > Articles > Lawn weed recognition based on improved Retinex algorithm Jiangsu Journal of Agricultural Sciences 2021,37 (6) 1417-1424

基于改进的Retinex算法的草坪杂草识别

作  者：
化春键;张爱榕;陈莹
单  位：
江南大学机械工程学院;江南大学物联网工程学院;江苏省食品先进制造装备技术重点实验室
关键词：
图像增强;局部方差;局部密度;Retinex算法;草坪杂草
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%。
译  名：
Lawn weed recognition based on improved Retinex algorithm
作  者：
HUA Chun-jian;ZHANG Ai-rong;CHEN Ying;School of Mechanical Engineering, Jiangnan University;Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment & Technology;School of Internet of Things Engineering, Jiangnan University;
关键词：
image enhancement;;local variance;;local density;;Retinex algorithm;;lawn weed
摘  要：
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.

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