Fast single image dehazing combined with adaptive haze estimation

Yan YANG; Long-long LIU; De-xin ZHANG; Zhi-fei YANG

doi:10.3788/OPE.20192710.2263

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Fast single image dehazing combined with adaptive haze estimation

Information Sciences | 更新时间：2020-08-13

- Fast single image dehazing combined with adaptive haze estimation
- Optics and Precision Engineering Vol. 27, Issue 10, Pages: 2263-2271(2019)
- 作者机构：
  
  兰州交通大学电子与信息工程学院，甘肃兰州730070
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20192710.2263
  CLC： TP391.41
- Received：20 March 2019，
  
  Accepted：10 April 2019，
  
  Published：15 October 2019
- 稿件说明：
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Yan YANG, Long-long LIU, De-xin ZHANG, et al. Fast single image dehazing combined with adaptive haze estimation[J]. Optics and precision engineering, 2019, 27(10): 2263-2271.
DOI：

Yan YANG, Long-long LIU, De-xin ZHANG, et al. Fast single image dehazing combined with adaptive haze estimation[J]. Optics and precision engineering, 2019, 27(10): 2263-2271. DOI： 10.3788/OPE.20192710.2263.

摘要

大气中水分子及微小颗粒对光的散射和吸收，使得在雾天条件下获取的图像严重降质，本文提出一种结合自适应雾气估计的快速单幅图像去雾算法。首先，该算法从大气散射模型出发，通过分析景深与亮度分量之间存在的相关关系，提出线性系数利用亮度分量来近似估计出景深，并通过最小滤波对明亮区域进行修正，得到粗略透射率；其次，观察到散射系数值与雾浓度呈正相关，从而结合雾浓度模型与指数函数提出自适应散射系数概念，估计出较准确的透射率；最后，根据大气散射模型复原出无雾图像。实验结果表明本文算法可以复原出清晰自然的无雾图像，明显提高了图像可见度，且具有较低的时间复杂度。

Abstract

Owing to the scattering and absorption of light by water molecules and other tiny particles in the atmosphere

images captured in hazy conditions are inevitably seriously degraded. To address this problem

a fast single image dehazing algorithm combined with adaptive haze estimation was proposed in this paper. The algorithm begined by considering the atmospheric scattering model and assumed a correlation between scene depth and luminance components. Subsequently

a linear coefficient was proposed to approximate the scene depth using the luminance components

and overly bright regions were corrected via minimum filtering to obtain a coarse transmission. As the value of the scattering coefficient was related to the haze concentration

the concept of an adaptive scattering coefficient was proposed by combining the haze concentration model with the exponential function to estimate the accurate transmission. Finally

a haze-free image was restored via the atmospheric scattering model. Experimental results demonstrated that the proposed algorithm could recover a clear

natural

and haze-free image

which significantly improves image visibility and has lower computational complexity than existing methods.

关键词

Keywords

references

HE L Y, ZHAO J H, ZHENG N N, et al .. Haze removal using the difference-structure-preservation prior[J]. IEEE Transactions on Image Process , 2017, 26(3): 1063-1075.

范之国, 宋强, 代晴晴, 等.全局参数估计的水下目标偏振复原方法[J].光学精密工程, 2018(7):1621-1632.

FAN ZH G, SONG Q, DAI Q Q, et al .. Underwater target polarization recovery method based on global parameter estimation[J]. Opt.Precision Eng., 2018(7):1621-1632.(in Chinese)

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

杨楠, 南琳, 张丁一, 等.基于深度学习的图像描述研究[J].红外与激光工程, 2018(2): 9-16.

YANG N, NAN L, ZHANG D Y, et al .. Research on interpretation based on deep learing[J]. Infrared and Laser Engineering , 2018(2): 1621-1632. (in Chinese)

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

LI M D, LIU J Y, YANG W H, et al .. Structure-revealing low-light image enhancement via robust retinex model[J]. IEEE Transactions on Image Processing, 2018, 27(6): 2828-2841.

HUANG S C, YEH C H. Image contrast enhancement for preserving mean brightness without losing image features[J]. Engineering Applications of Artificial Intelligence, 2013, 26(5/6):1487-1492.

HE K M, SUN J, TANG X O. Guided image filtering[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2013, 35(6): 1397-1409.

NARASIMHAN S G, NAYAR S K. Interactive (de) weathering of an image using physical models[J]. IEEE Workshop Color Photometric Methods Comput , 2003, 6: 1-8.

TANG K, YANG J, WANG J. Investigating haze-relevant features in a learning framework for image dehazing[C]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014: 2995-3002.

MENG G F, WANG Y, DUAN J Y, et al .. Efficient image dehazing with boundary constraint and contextual regularization[C]. IEEE International Conference on Computer Vision, 2013: 617-624.

ZHU Q S, MAI J, SHAO L. A Fast single image haze removal algorithm using color attenuation prior[J]. IEEE Transactions on Image Processing, 2015, 24(11): 3522-3533.

汤春明, 董燕成, 孙欣, 等.单幅夜间弱照度雾霾雾霾图像的复原算法[J].计算机辅助设计与图形学学报, 2018, 30(3):459-467.

TANG CH M, DONG Y CH, SUN X, et al ..Image restoration algorithm for single nighttime weakly illuminated haze image[J]. Journal of Computer-Aided Design &Computer Graphics, 2018, 30(3): 459-467. (in Chinese)

SUN W, WANG H, SUN C H, et al .. Fast single image haze removal via local atmospheric light veil estimation[J]. Computers & Electrical Engineering, 2015, 46(C): 371-383.

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

王一斌, 伊诗白, 吕卓纹.自适应背景光估计与非局部先验的水下图像复原[J].光学精密工程, 2019, 27(2):499-509.

WANG Y B, YI SH B, LV ZH W. Under image restoration with adaptive background light estimation and non-local prior[J]. Opt.Precision Eng., 2019, 27(2):499-509. (in Chinese)

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