结合背景光融合及水下暗通道先验和色彩平衡的#br# 水下图像增强

doi:10.16451/j.cnki.issn1003-6059.201809008

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摘要

水下环境、光线衰减和拍摄方式造成水下图像具有不同色调、对比度和模糊度.基于图像成像模型的水下图像复原方法通常基于暗通道先验或最大像素先验,容易受到水下复杂环境的干扰而输出低质量的复原图像,因此文中提出基于背景光融合及水下暗通道先验和色彩平衡的水下图像增强方法.首先,提出多候选背景光融合方法,估计正确的背景光.然后,基于高质量水下图像统计得出水下暗通道先验,计算更准确的RGB分量传输地图.将复原图像从RGB颜色模型转换到CIE-Lab颜色模型,对L亮度分量和a、b色彩分量分别进行归一化拉伸和优化调整,进一步提高复原后水下图像的亮度和对比度.多种定性和定量分析说明文中方法增强的图像在对比度、亮度和颜色上的显示效果优于大部分现有的水下图像增强方法复原的图像.

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	宋巍
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关键词 ：水下图像增强, 背景光融合, 水下暗通道先验(UDCP), 图像成像模型(IFM), 色彩平衡

Abstract：

Different hues, contrasts and blurriness of underwater images are caused by underwater environment, light attenuation and photography methods. The dark channel prior(DCP) or the maximum intensity prior(MIP) is often utilized in the underwater image restoration methods based on the image formation model(IFM). Low-quality restored images are produced by these methods, and they are easily disturbed by the complicated underwater environment. The method of image restoration using background light fusion and underwater DCP and image color balancing for underwater image enhancement is proposed in this paper. Firstly, a correct background light(BL) is estimated through the fusion of multiple candidate background lights.Then, an underwater dark channel prior(UDCP) is determined based on the statistics of a large number of high-quality(HQ) underwater images and accurate RGB transmission maps are finally obtained. The restored image in the RGB color model is transformed to a CIE-Lab color model, and the ‘L’ luminance component and color components ‘a’ ‘b’ are conducted with the normalized stretching and optimal modification respectively to further improve the brightness and contrast of the restored image. Various qualitative and quantitative assessments are applied to demonstrate that the proposed method is better than the state-of-the-art restoration methods in contrast, brightness and color.

Key words： Underwater Image Enhancement Background Light Fusion Underwater Dark Channel Prior(UDCP) Image Formation Model(IFM) Color Balancing

收稿日期: 2018-05-02

ZTFLH:

TP 391

基金资助:

国家自然科学基金项目(No.61702323,41671431)、上海市高校特聘教授(东方学者)项目(No.TP2016038)、上海海洋大学博士科研启动基金项目(No.A2-0203-17-100322)资助

作者简介: 黄冬梅（通讯作者），硕士，教授，主要研究方向为海洋信息化、数据挖掘、数据库技术.E-mail：dmhuang@shou.edu.cn.贺琪，博士，副教授，主要研究方向为海洋大数据存储、工作流与业务流程管理、服务计算、云计算.E-mail：qhe@shou.edu.cn.王振华，博士，副教授，主要研究方向为空间质量控制研究.E-mail：zhwang@shou.edu.cn.

引用本文:

宋巍，王龑，黄冬梅，贺琪，王振华. 结合背景光融合及水下暗通道先验和色彩平衡的#br# 水下图像增强[J]. 模式识别与人工智能, 2018, 31(9): 856-868. SONG Wei, WANG Yan, HUANG Dongmei, HE Qi, WANG Zhenhua. Combining Background Light Fusion and Underwater Dark Channel Prior with Color Balancing for Underwater Image Enhancement. , 2018, 31(9): 856-868.

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