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基于光照-反射成像模型和形态学操作的多谱段图像增强算法
引用本文:王殿伟,韩鹏飞,范九伦,刘颖,许志杰,王晶.基于光照-反射成像模型和形态学操作的多谱段图像增强算法[J].物理学报,2018,67(21):210701-210701.
作者姓名:王殿伟  韩鹏飞  范九伦  刘颖  许志杰  王晶
作者单位:1. 西安邮电大学通信与信息工程学院, 西安 710121;2. 电子信息现场勘验应用技术公安部重点实验室, 西安 710121;3. (英国)哈德斯菲尔德大学计算机与工程学院, 哈德斯菲尔德 HD1 3DH;4. (英国)谢菲尔德哈雷姆大学计算机学院, 谢菲尔德 S1 1WB
基金项目:国家自然科学基金(批准号:61671377)、2018陕西省自然科学基础研究计划科技创新创业"双导师"项目(批准号:2018JM6118)、2018西安邮电大学创新创业项目(批准号:2018SC-08)"(批准号:CXJJ2017057)资助的课题.
摘    要:为解决多谱段降质图像增强问题,提出了一种基于光照-反射成像模型和形态学操作的多谱段图像增强算法.首先对图像饱和度使用自适应非线性拉伸函数进行拉伸,使增强后的图像色彩更加饱和、自然;接下来利用引导滤波算法提取出图像的光照分量,提出了一种基于细节特征的加权融合策略,利用光照分布特性构造了一种自适应Gamma校正函数对光照分量进行处理,并将其与利用对比度受限的自适应直方图均衡化方法处理后的光照分量以及原始光照分量进行融合;然后在反射分量校正时,构造了一种形态学操作函数来校正反射信息;最后合并光照分量和反射分量,并与处理后的饱和度分量和色调分量一起得到增强图像.采用主客观评价指标对可见光低照度图像、水下图像、高动态范围图像、沙尘暴图像、雾天图像和热红外图像6种降质多谱段图像实验结果进行分析比较,结果表明本文算法能够有效地抑制图像噪声、增强图像细节信息、改善图像视觉效果,可应用于多种图像增强领域.

关 键 词:多谱段图像增强  细节特征  引导滤波  形态学操作
收稿时间:2018-07-04

Multispectral image enhancement based on illuminance-reflection imaging model and morphology operation
Wang Dian-Wei,Han Peng-Fei,Fan Jiu-Lun,Liu Ying,Xu Zhi-Jie,Wang Jing.Multispectral image enhancement based on illuminance-reflection imaging model and morphology operation[J].Acta Physica Sinica,2018,67(21):210701-210701.
Authors:Wang Dian-Wei  Han Peng-Fei  Fan Jiu-Lun  Liu Ying  Xu Zhi-Jie  Wang Jing
Institution:1. School of Communications and Information Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China;2. Key laboratory of Electronic Information Application Technology for Scene Investigation, Ministry of Republic Security, Xi'an 710121, China;3. School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK;4. Department of Computing, Sheffield Hallam University, Sheffield S1 1WB, UK
Abstract:In this paper we propose a multispectral image enhancement algorithm based on illuminance-reflection imaging model and morphology operation that enables us to solve the problem of improving the multispectral degraded images. Firstly, we transform the image from RGB space to HSV color space, and the hue remains unchanged. As for the saturation component, we use the adaptive nonlinear stretching to improve the image color saturation and brightness. Secondly, according to the illuminance-reflection imaging model, we adopt the guided image filtering method to decompose the brightness into illuminance component and reflection component. Usually, the illumination component mainly determines the dynamic range of the pixels in the image, corresponding to the low frequency part of the image, reflecting the global characteristics of the image and the edge detail information of the image; the reflected component represents the intrinsic essential characteristics of the image, corresponding to the high frequency part of the image, and contains most of the local detail information of the image as well as all noise. Thirdly, we present an improved adaptive gamma function, which can dynamically adjust the illuminance component by the local distribution characteristics, and use the contrast-limited adaptive histogram equalization to correct the illuminance component. Afterwards we propose a detail-feature weighted fusion strategy. The original illumination and the two corrected illuminations are fused to obtain the final illumination component. Fourthly, we propose an improved morphological operation to denoise and enhance the details of the reflection component. Finally, the corrected illumination component and the enhanced reflection component are combined to obtain the improved brightness component. In order to verify the efficiency of the algorithm proposed in the paper, we use both subjective visual effectiveness method and quantitative parameter analysis method to measure the enhancement performance in multispectral imaging scenarios, including low illumination image, underwater image, high-dynamic range image, sandstorm image, haze image and thermal infrared image. Then standard deviation, information entropy and average gradient are used as evaluation indices respectively, and qualitative and quantitative comparison with a variety of image enhancement algorithms show that the proposed algorithm can not only well suppress noise but also obviously improve local details and global contrast. Experimental results show that the proposed method proves to be better in performance.
Keywords:multispectral image enhancement  detailed-features  guided image filter  morphological operation
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