[1]廖娟,陈民慧,汪鹞,等.基于双重Gamma校正的秧苗图像增强算法[J].江苏农业学报,2020,(06):1411-1418.[doi:doi:10.3969/j.issn.1000-4440.2020.06.009]
　LIAO Juan,CHEN Min-hui,WANG Yao,et al.Image enhancement algorithm for seedling image with dual gamma correction[J].,2020,(06):1411-1418.[doi:doi:10.3969/j.issn.1000-4440.2020.06.009]
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基于双重Gamma校正的秧苗图像增强算法()
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江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2020年06期
页码:
1411-1418
栏目:
耕作栽培·资源环境
出版日期:
2020-12-31

文章信息/Info

Title:
Image enhancement algorithm for seedling image with dual gamma correction
作者:
廖娟1; 陈民慧1; 汪鹞1; 邹禹2; 张顺1; 张培江2; 朱德泉1
(1.安徽农业大学工学院，安徽合肥230036;2.安徽省农业科学院水稻研究所，安徽合肥230031)
Author(s):
LIAO Juan1; CHEN Min-hui1; WANG Yao1; ZOU Yu2; ZHANG Shun1; ZHANG Pei-jiang2; ZHU De-quan1
(1.School of Engineering, Anhui Agricultural University, Hefei 230036， China；2.Rice Research Institute， Anhui Academy of Agricultural Sciences， Hefei 230031, China)
关键词:
秧苗图像; 图像增强; 自适应Gamma校正; 色彩饱和度修复
Keywords:
seedling image; image enhancement; adaptive gamma correction; color saturation restoration
分类号:
TP391.4
DOI:
doi:10.3969/j.issn.1000-4440.2020.06.009
文献标志码:
A
摘要:
为了提高复杂光照条件下水田秧苗图像的视觉效果，提出1种基于双重Gamma校正的秧苗图像增强算法。将原始RGB[红(R)-绿(G)-蓝(B)]图像转换成HSV[色调(H)-饱和度(S)-亮度(V)]颜色空间图像，对V分量进行亮度区域划分；通过快速引导滤波法提取秧苗图像的光照度分量，利用光照信息的分布特性自适应地设置Gamma控制参数，并构建2个自适应Gamma函数，实现对V分量图像亮度的独立校正；最后，对校正后的图像进行自适应融合，并结合H、S分量转换为RGB图像，进行色彩饱和度的恢复。结果表明，本研究算法能够实现不同光照条件下秧苗图像的自适应增强，丰富图像中的有用信息，保真原图像的色彩信息，有效改善图像的视觉效果，为后期的秧苗分割提供可靠的处理对象。
Abstract:
To improve the visual effect of seedling images under complex illumination conditions, an image enhancement algorithm based on dual gamma correction was proposed. The original RGB image was firstly converted into HSV color space image, and the V component was extracted and partitioned into dark and bright regions. The illumination component of seedling image was obtained by the fast guided filtering method. The gamma control parameters were dynamically adjusted by the local distribution characteristics of illumination, and two adaptive gamma functions were constructed to correct the luminance of V-component image. Then, the corrected images were fused adaptively. Combining with H and S components, the fused V image was used to obtain the RGB image. Finally, the color saturation restoration was carried out for RGB image to solve the color deviation. Experimental results show that the algorithm can achieve adaptive enhancement of seedling images under different lighting conditions, enrich the useful information in the image, maintain the color information of the original image, effectively improve the visual effect of the image, and provide reliable processing objects for seedling segmentation.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期：2020-09-08基金项目：国家重点研发计划项目(2018YFD0700304)；安徽省重点研发计划项目(202004a06020016)；安徽省科技重大专项(18030701204)作者简介：廖娟(1986-)，女，安徽安庆人，博士，讲师，硕士生导师，主要从事计算机视觉技术研究。(E-mail)liaojuan308@163.com通讯作者：朱德泉，(E-mail)zhudequan@ahau.edu.cn
更新日期/Last Update: 2021-01-15