An Image Dehazing Method Combining Adaptive Brightness Transformation Inequality to Estimate Transmittance

doi:10.7652/xjtuxb202106009

您当前的位置：

首页 >

文章列表页 >

An Image Dehazing Method Combining Adaptive Brightness Transformation Inequality to Estimate Transmittance

更新时间：2025-07-09

- An Image Dehazing Method Combining Adaptive Brightness Transformation Inequality to Estimate Transmittance
- Vol. 55, Issue 6, Pages: 69-76(2021)
- 作者机构：
  
  兰州交通大学电子与信息工程学院,兰州,730070
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202106009
  CLC： TM391
- Online First：10 June 2021，
  
  Published：2021
- 稿件说明：
移动端阅览
An Image Dehazing Method Combining Adaptive Brightness Transformation Inequality to Estimate Transmittance[J]. 2021, 55(6): 69-76.
DOI：

An Image Dehazing Method Combining Adaptive Brightness Transformation Inequality to Estimate Transmittance[J]. 2021, 55(6): 69-76. DOI： 10.7652/xjtuxb202106009.

摘要

为了解决单幅图像去雾算法中透射率估计不准确和去雾不彻底问题

提出了一种结合自适应亮度变换不等式估计透射率的图像去雾方法。首先

利用亮度和反饱和度进行亮度变换不等式补偿构造场景深度

并使用对数变换进行灰度扩展; 其次

采用正则化对场景深度优化

解决相邻像素的差异过大问题

逼近精确场景深度; 然后

根据局部恒定假设

利用局部平均深度和联合双边滤波估计动态大气散射系数

结合场景深度和动态大气散射系数获得透射率; 最后

使用中值滤波和区间估值优化局部大气光

并通过大气散射模型恢复无雾图像。实验结果表明:所提出的图像去雾方法可在不同场景自适应地调节模型参数

去雾彻底

获得了较好的去雾效果; 同时

客观指标中平均视觉对比度达到了60.572

具有较好的图像保真度。

Abstract

An image dehazing method combining adaptive brightness transformation inequality to estimate transmittance is proposed to solve the problems of inaccurate transmittance estimation and incomplete dehazing in single image dehazing algorithms. Firstly

the brightness transformation inequality is applied to construct the scene depth by using brightness and inverse saturation

and a logarithmic transformation is used to expand gray scale. Secondly

regularization is used to optimize scene depth to solve the excessive difference between adjacent pixels and to approximate the exact depth of the scene. Then

according to the local constant assumption

the dynamic atmospheric scattering coefficient is estimated using local average depth and joint bilateral filtering

and the transmittance is obtained by combining the scene depth and dynamic atmospheric scattering coefficient. Finally

the median filtering and interval estimation are used to optimize the local atmospheric light

and clear image is recovered by the atmospheric scattering model. Experimental results show that the proposed method can adaptively adjust the model parameters in different scenes

thoroughly remove haze

and get better dehazing effect. At the same time

the average visual contrast in objective indexes reaches 60.572

with good image fidelity.

关键词

Keywords

references

YANG Yan, ZHANG Chen, LIU Longlong, et al. Visibility restoration of single image captured in dust and haze weather conditions [J]. Multidimensional Systems and Signal Processing, 2019, 31: 619-633.

李佳童, 章毓晋. 图像去雾算法的改进和主客观性能评价 [J]. 光学精密工程, 2017, 25(3): 735-741.

LI Jiatong, ZHANG Yujin. Improvements of image haze removal algorithm and its subjective and objective performance evaluation [J]. Optics and Precision Engineering, 2017, 25(3): 735-741.

LIU Haoting, LU Hanqing, ZHANG Yu. Image enhancement for outdoor long-range surveillance using IQ-learning multiscale Retinex [J]. IET Image Processing, 2017, 11(9): 786-795.

刘海波, 杨杰, 吴正平, 等. 基于暗通道先验和Retinex理论的快速单幅图像去雾方法 [J]. 自动化学报, 2015, 41(7): 1264-1273.

LIU Haibo, YANG Jie, WU Zhengping, et al. A fast single image dehazing method based on dark channel prior and Retinex theory [J]. Acta Automatica Sinica, 2015, 41(7): 1264-1273.

XU Yong, WEN Jie, FEI Lunke, et al. Review of video and image defogging algorithms and related studies on image restoration and enhancement [J]. IEEE Access, 2016, 4: 165-188.

HE Kaiming, SUN Jian, TANG Xiaoou. Single image haze removal using dark channel prior [C]∥IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, USA: IEEE, 2009: 1956-1963.

HE Kaiming, SUN Jian, TANG Xiaoou. Guided image filtering [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(6): 1397-1409.

MENG Gaofeng, WANG Ying, DUAN Jiangyong, et al. Efficient image dehazing with boundary constraint and contextual regularization [C]∥IEEE International Conference on Computer Vision. Piscataway, NJ, USA: IEEE, 2013: 617-624.

ZHU Qingsong, MAI Jiaming, SHAO Ling. A fast single image haze removal algorithm using color attenuation prior [J]. IEEE Transactions on Image Processing, 2015, 24(11): 3522-3532.

WANG Wencheng, YUAN Xiaohui, WU Xiaojin, et al. Fast image dehazing method based on linear transformation [J]. IEEE Transactions on Multimedia, 2017, 19(6): 1142-1155.

REN Wenqi, LIU Si, ZHANG Hua, et al. Single image dehazing via multi-scale convolutional neural networks [M]∥Computer Vision: ECCV 2016. Berlin, Germany: Springer International Publishing, 2016: 154-169.

CAI Bolun, XU Xiangmin, JIA Kui, et al. DehazeNet: an end-to-end system for single image haze removal [J]. IEEE Transactions on Image Processing, 2016, 25(11): 5187-5198.

NARSAIHAN S, NAYAR S. Vision and the atmosphere [J]. International Journal of Computer Vision, 2002, 48(3): 233-254.

WANG Wencheng, YUAN Xiaohui. Recent advances in image dehazing [J]. IEEE/CAA Journal of Automatica Sinica, 2017, 4(3): 410-436.

KUMARI A, SAHDEV S, SAHOO S K. Improved single image and video dehazing using morphological operation [C]∥International Conference on VLSI Systems. Piscataway, NJ, USA: IEEE,2015: 1-5.

SUN Wenhai, WANG Hao, SUN Changhao, et al. Fast single image haze removal via local atmospheric light veil estimation [J]. Computer Electrical Engineering, 2015, 46: 371-383.

黄文君, 李杰, 齐春. 低秩与字典表达分解的浓雾霾场景图像去雾算法 [J]. 西安交通大学学报, 2020, 54(4): 124-131.

HUANG Wenjun, LI Jie, QI Chun. A defogging algorithm for dense fog images via low-rank and dictionary expression decomposition [J]. Journal of Xi'an Jiaotong University, 2020, 54(4): 124-131.

CHOI L K, YOU J, BOVIK A. Referenceless prediction of perceptual fog density and perceptual image defogging [J]. IEEE Transactions on Image Processing, 2015, 24(11): 3888-3901.

杨红, 崔艳. 基于开运算暗通道和优化边界约束的图像去雾算法 [J]. 光子学报, 2018, 47(6): 244-250.

YANG Hong, CUI Yan. Image defogging algorithm based on opening dark channel and improved boundary constraint [J]. Acta Photonica Sinica, 2018, 47(6): 244-250.

金仙力, 张威, 刘林峰. 基于引导滤波和自适应容差的图像去雾算法 [J]. 通信学报, 2020, 41(5): 31-40.

JIN Xianli, ZHANG Wei, LIU Linfeng. Image defogging algorithm based on guided filtering and adaptive tolerance [J]. Journal on Communications, 2020, 41(5): 31-40.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

A Multi-Stage Transformer Network for Image Dehazing Based on Contrastive Learning

From Global to Local: A Dual-Attention Fusion Dehazing Network

A Model of Pulsed Photothermal Radiation for the Laser Treatment of Dermatological Diseases

I/O Performance Modeling and Prediction Method for Large-Scale Parallel Applications Using Machine Learning

Bi-Level Multi-Obj ective Optimization of a Wind-Solar Coupled Hydrogen Production System Based on the Synergy of Hybrid Electrolyzer Arrays

Related Author

GAO Feng

JI Shengchang

GUO Jie

HOU Jie

OUYANG Chao

YANG Biao

YANG Aiwei

WANG Huake

Related Institution

School of Electrical Engineering, Xi'an Jiaotong University

School of Computer Science, Harbin Institute of Technology(Shenzhen)

School of Architecture, Harbin Institute of Technology(Shenzhen),,,)

(Faculty of Electronic and Information Engineering, Xi'an Jiaotong University,,)

State Key Laboratory of Multiphase Flow in Power Engineering, Xi'an Jiaotong University

⁰