改进直方图匹配和自适应均衡的水下图像增强

周辉奎; 章立; 胡素娟

改进直方图匹配和自适应均衡的水下图像增强

江西旅游商贸职业学院艺术传媒与计算机学院, 江西南昌 330100

基金项目:

江西省高等学校教学改革研究课题 JXJG-20-50-9

详细信息

作者简介:
周辉奎（1983-），男，江西崇仁人，硕士，副教授，研究方向为图像处理与软件技术。E-mail: 251856962@qq.com

中图分类号: TP391
计量
- 文章访问数: 98
- HTML全文浏览量: 62
- PDF下载量: 41
出版历程
- 收稿日期: 2023-03-29
- 修回日期: 2023-11-06
- 网络出版日期: 2024-05-23
- 刊出日期: 2024-05-19

Underwater Image Enhancement Based on Improved Histogram Matching and Adaptive Equalization

School of Art Media and Computer, Jiangxi Tourism & Commerce Vocational College, Nanchang 330100, China

摘要

摘要:
为了更有效地改善水下图像的颜色，进一步提升图像的对比度和清晰度，提出改进直方图匹配和自适应均衡的水下图像增强方法。以像素均值最大的通道图像的直方图作为基准，对各通道图像分别进行直方图匹配，校正水下图像的颜色偏差；充分利用HSI颜色空间中颜色分量与明度分量的独立性，对明度分量进行自适应的局部直方图均衡化，进一步提升图像的对比度和清晰度。主、客观的实验数据显示，相对于部分现有方法，本文方法对水下图像增强后的视觉效果更优，信息熵、平均梯度、水下图像质量指标（Underwater Image Quality Measures, UIQM）和结构相似性指数（Structural Similarity Index Measure, SSIM）的值更高。因此，本文方法对水下图像具有更有优的增强效果。
- 水下图像增强 /
- 直方图匹配 /
- 局部直方图均衡 /
- 水下图像质量指标
Abstract:
To improve the color of underwater imaging more effectively, and enhance the contrast and clarity of images, an underwater image enhancement method based on improved histogram matching and adaptive equalization is proposed. Each channel image is subjected to histogram matching using the histogram of the channel image with the largest pixel mean as the benchmark to correct the color deviation of the underwater image; taking full advantage of the independence of the color and lightness components in the HSI color space, this method performs an adaptive local histogram equalization on the lightness component, further improving the contrast and clarity of the image. Subjective and objective experimental data show that compared with some existing methods, the proposed method achieves better visual effects on underwater images after enhancement, with higher information entropy, an average gradient, UIQM, and SSIM. Therefore, the proposed method has a better enhancement effect on underwater images.
- underwater image enhancement /
- histogram matching /
- local histogram equalization /
- UIQM

HTML全文

图 1 空气成像与水下成像的直方图分布

Figure 1. Histogram distribution of air imaging and underwater imaging

下载: 全尺寸图片幻灯片

图 2 直方图匹配的效果

Figure 2. Effect of histogram matching

下载: 全尺寸图片幻灯片

图 3 直方图匹配和局部直方图均衡化的效果

Figure 3. Effect of histogram matching and local histogram equalization

下载: 全尺寸图片幻灯片

图 4 水下图像增强的视觉效果

Figure 4. Visual effect of enhanced underwater images

下载: 全尺寸图片幻灯片

图 5 水下图像增强的信息熵

Figure 5. Information entropy of enhanced underwater images

下载: 全尺寸图片幻灯片

图 6 水下图像增强的平均梯度

Figure 6. Average gradient of enhanced underwater images

下载: 全尺寸图片幻灯片

图 7 水下图像增强的UIQM

Figure 7. UIQM of enhanced underwater images

下载: 全尺寸图片幻灯片

图 8 本文方法与基准方法Water-Net的增强效果比较

Figure 8. Comparison of enhancement effects between the proposed method and the benchmark method Water-Net

下载: 全尺寸图片幻灯片

表 1 水下图像增强的SSIM

Table 1 SSIM of enhanced underwater images %

Scene	Original	Ref.[6]	Ref.[10]	Ref.[11]	Ref.[13]	Proposed
Two	75.4	94.8	82.9	87.3	84.7	95.2
Three	66.1	87.5	78.3	85.9	84.5	89.3
Four	72.2	95.2	86.3	87.4	88.4	98.6
Five	76.6	87.7	85.4	82.9	86.9	89.4

下载: 导出CSV

参考文献(17)

[1]	马敏慧, 王红茹, 王佳. 基于改进的MSRCR-CLAHE融合的水下图像增强算法[J]. 红外技术, 2023, 45(1): 23-32. http://hwjs.nvir.cn/cn/article/id/69e5b90e-9c0c-43c6-b4e2-dedede3eb414 MA M H, WANG H R, WANG J. An underwater image enhancement algorithm based on improved MSRCR-CLAHE fusion[J]. Infrared Technology, 2023, 45(1): 23-32. http://hwjs.nvir.cn/cn/article/id/69e5b90e-9c0c-43c6-b4e2-dedede3eb414
[2]	卫依雪, 周冬明, 王长城, 等. 采用多尺度密集残差网络的水下图像增强[J]. 无线电工程, 2021, 51(9): 870-878. DOI: 10.3969/j.issn.1003-3106.2021.09.005 WEI Y X, ZHOU D M, WANG C C, et al. Underwater image enhancement using multi-scale dense residual network[J]. Radio Engineering, 2021, 51(9): 870-878. DOI: 10.3969/j.issn.1003-3106.2021.09.005
[3]	PENG Y T, Cosman P C. Underwater image restoration based on image blurriness and light absorption[J]. IEEE Transactions on Image Processing, 2017, 26(4): 1579-1594. DOI: 10.1109/TIP.2017.2663846
[4]	WANG Y, LIU H, CHAU L P. Single underwater image restoration using adaptive attenuation-curve prior[J]. IEEE Transactions on Circuits and Systems I: Regular Papers, 2018, 65(3): 992-1002. DOI: 10.1109/TCSI.2017.2751671
[5]	PENG Y T, CAO K, Cosman P C. Generalization of the dark channel prior for single image restoration[J]. IEEE Transactions on Image Processing, 2018, 27(6): 2856-2868. DOI: 10.1109/TIP.2018.2813092
[6]	LIN S, CHI K, WEI T, et al. Underwater image sharpening based on structure restoration and texture enhancement[J]. Applied Optics, 2021, 60(15): 4443-4454. DOI: 10.1364/AO.420962
[7]	CHIANG J Y, CHEN Y C. Underwater image enhancement by wavelength compensation and dehazing[J]. IEEE Transactions on Image Processing, 2012, 21(4): 1756-1769. DOI: 10.1109/TIP.2011.2179666
[8]	黄冬梅, 王龑, 宋巍, 等. 不同颜色模型下自适应直方图拉伸的水下图像增强[J]. 中国图象图形学报, 2018, 23(5): 640-651. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTB201805003.htm HUANG D M, WANG Y, SONG W, et al. Underwater image enhancement method using adaptive histogram stretching in different color models[J]. Journal of Image and Graphics, 2018, 23(5): 640-651. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTB201805003.htm
[9]	ZHANG S, WANG T, DONG J, Yu H. Underwater image enhancement via extended multi-scale Retinex[J]. Neurocomputing, 2017, 245: 1-9. DOI: 10.1016/j.neucom.2017.03.029
[10]	史金余, 郝明良, 邹沛煜. 基于颜色校正和引导滤波分层的水下图像增强[J]. 计算机应用与软件, 2022, 39(6): 203-209. DOI: 10.3969/j.issn.1000-386x.2022.06.030 SHI J Y, HAO M L, ZOU P Y. Underwater image enhancement based on color correction and guided filtering layering[J]. Computer Applications and Software, 2022, 39(6): 203-209. DOI: 10.3969/j.issn.1000-386x.2022.06.030
[11]	Ulutas G, Ustubioglu B. Underwater image enhancement using contrast limited adaptive histogram equalization and layered difference representation[J]. Multimedia Tools and Applications, 2021, 80: 15067-15091. DOI: 10.1007/s11042-020-10426-2
[12]	GUO Y, LI H, ZHUANG P. Underwater image enhancement using a multiscale dense generative adversarial network[J]. IEEE Journal of Oceanic Engineering, 2020, 45(3): 862-870. DOI: 10.1109/JOE.2019.2911447
[13]	LI H, ZHUANG P. DewaterNet: A fusion adversarial real underwater image enhancement network[J]. Signal Processing: Image Communication, 2021, 95: 116248. DOI: 10.1016/j.image.2021.116248.
[14]	WANG B, KANG Z, DONG P, et al. Underwater image enhancement by maximum-likelihood based adaptive color correction and robust scattering removal[J]. Frontiers of Computer Science, 2023, 17(2): 172702. DOI: 10.1007/s11704-022-1205-7
[15]	翟海祥, 何嘉奇, 王正家, 等. 改进Retinex与多图像融合算法用于低照度图像增强[J]. 红外技术, 2021, 43(10): 987-993. http://hwjs.nvir.cn/cn/article/id/23500140-4bab-40b3-9bef-282f14dc105e ZHAI H X, HE J Q, WANG Z J, et al. Improved Retinex and multi-image fusion algorithm for low illumination image enhancement[J]. Infrared Technology, 2021, 43(10): 987-993. http://hwjs.nvir.cn/cn/article/id/23500140-4bab-40b3-9bef-282f14dc105e
[16]	Panetta K, Gao C, Agaian S. Human-visual-system-inspired underwater image quality measures[J]. IEEE Journal of Oceanic Engineering, 2016, 41(3): 541-551. DOI: 10.1109/JOE.2015.2469915
[17]	LI C, GUO C, REN W, et al. An underwater image enhancement benchmark dataset and beyond[J]. IEEE Transactions on Image Processing, 2019, 29: 4376-4389.

施引文献(32)

期刊类型引用(7)

1.	张远利. 短波红外成像技术在军事上的应用进展. 应用激光. 2024(04): 171-177 . 百度学术
2.	吴海清，谈大伟. 防辐射长波红外连续变焦光学系统设计. 红外. 2023(02): 1-7 . 百度学术
3.	Qian-ran Hu，Xing-yu Shen，Xin-ming Qian，Guang-yan Huang，Meng-qi Yuan. The personal protective equipment (PPE) based on individual combat:A systematic review and trend analysis. Defence Technology. 2023(10): 195-221 . 必应学术
4.	吴海清，王朋. 小型化大面阵非制冷红外连续变焦光学系统设计. 红外. 2020(02): 1-6 . 百度学术
5.	吴海清. 防辐射非制冷红外二次成像光学系统设计. 光学与光电技术. 2020(06): 60-65 . 百度学术
6.	杨敏，徐文斌，田禹泽，孙振远，孙宪中. 面向运动目标探测的分时型红外偏振成像系统. 光学学报. 2020(15): 70-77 . 百度学术
7.	吴海清，曾宪宇，王朋. 微型长波红外无热化光学系统设计. 红外. 2019(03): 1-5 . 百度学术