结合改进红通道先验与幂律校正CLAHE的水下图像复原方法

朱佳琦; 周丽丽; 闫晶晶; 王桥桥; 蒋玉红; 何立风

结合改进红通道先验与幂律校正CLAHE的水下图像复原方法

1.
陕西科技大学电气与控制工程学院，陕西西安 710021
2.
陕西科技大学电子信息与人工智能学院，陕西西安 710021

详细信息

作者简介:
朱佳琦（1996-），男，硕士研究生，主要研究方向为图像处理。E-mail: zhujiaqi1996@outlook.com

通讯作者:
周丽丽（1981-），女，博士，副教授，主要研究方向为电子科学与技术。E-mail: zhoulili@sust.edu.cn

中图分类号: TP391
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- 文章访问数: 420
- HTML全文浏览量: 137
- PDF下载量: 41
- 被引次数: 0
出版历程
- 收稿日期: 2020-06-09
- 修回日期: 2020-10-24
- 刊出日期: 2021-07-01

Underwater Image Restoration Method Combining Improved Red Channel Prior and Power Law Correction-based CLAHE Algorithm

1.
School of Electrical and Control Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China
2.
School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi'an 710021, China

摘要

摘要: 针对水下降质图像复原过程中，存在背景光预估偏差及对比度失衡的问题，提出一种水下图像复原方法。首先根据超像素图像分割方法确定背景光区域及取值，然后采用红通道先验理论求取预估透射率，获得初步复原图像；最终通过归一化幂律校正的限制对比度自适应直方图均衡化（Contrast Limited Adaptive Histogram Equalization，CLAHE）算法增强复原图像的颜色。使用3种图像质量评价标准对实验结果进行客观分析，结果表明，该方法可以有效均衡对比度，提高可视化效果。
- 水下图像复原 /
- 背景光预估 /
- 红通道先验 /
- CLAHE /
- 颜色校正
Abstract: An underwater image restoration method was proposed to address the issues of background light estimation deviation and contrast imbalance that occurs during the restoration of water degradation images. First, the background light area and value are determined according to the superpixel image segmentation method, and then the red channel prior theory is used to obtain the estimated transmittance and the preliminary restored image. Finally, the color of the restored image is enhanced using the normalized power law correction-based contrast limited adaptive histogram equalization (CLAHE) algorithm. Three image quality evaluation standards are used to objectively analyze the experimental results, and it is found that the proposed method can effectively balance the contrast and improve the visualization effect.
- underwater image restoration /
- background light estimation /
- red channel prior /
- CLAHE /
- color correction

HTML全文

图 1 水下光学成像模型

Figure 1. Underwater optical imaging model

下载: 全尺寸图片幻灯片

图 2 本文所提算法流程

Figure 2. The algorithm flow proposed in this article

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图 3 超像素分割及其二值化区域邻接图

Figure 3. Superpixel segmentation and Binary area adjacency graph

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图 4 海底地面

Figure 4. Sea floor

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图 5 珊瑚

Figure 5. Coral

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图 6 鱼群

Figure 6. Fishes

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图 7 潜水员

Figure 7. Diver

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表 1 各项评价指标值

Table 1. Evaluation index values

Image name	Algorithm	Information entropy	Average gradient	MCMA
Sea floor	Original image	11.889 4	0.671 4	-
	Scene depth prior	12.266 2	0.833 2	0.466 1
	Fusion	14.846 2	1.357 6	0.572 6
	Red channel prior	13.981 7	1.198 1	0.545 0
	Proposed algorithm	15.031 6	2.450 8	0.579 0
Coral	Original image	14.797 1	2.712 6	-
	Scene depth prior	15.438 5	3.162 8	0.635 9
	Fusion	15.759 0	2.839 1	0.603 4
	Red channel prior	16.060 6	3.765 4	0.618 3
	Proposed algorithm	16.746 3	5.365 3	0.670 1
Fishes	Original image	11.622 0	1.170 2	-
	Scene depth prior	14.890 8	2.687 7	0.589 4
	Fusion	15.221 2	4.395 0	0.719 7
	Red channel prior	15.385 4	3.436 3	0.711 6
	Proposed algorithm	15.948 7	6.651 1	0.764 5
Diver	Original image	13.940 8	1.369 7	-
	Scene depth prior	14.573 1	1.793 6	0.596 5
	Fusion	15.522 6	2.835 6	0.716 9
	Red channel prior	14.917 5	1.633 1	0.608 5
	Proposed algorithm	15.527 2	2.846 9	0.661 3

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参考文献(14)

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