Improved multi-scale Retinex infrared image enhancement

Ran-ran WEI; Wei-da ZHAN; De-peng ZHU; Yong TIAN

doi:10.37188/CJLCD.2020-0109

您当前的位置：

首页 >

文章列表页 >

Improved multi-scale Retinex infrared image enhancement

Image Processing | 更新时间：2021-03-23

- Improved multi-scale Retinex infrared image enhancement
- Chinese Journal of Liquid Crystals and Displays Vol. 36, Issue 3, Pages: 465-474(2021)
- 作者机构：
  
  长春理工大学电子信息工程学院, 吉林长春 130022
- 作者简介：
- 基金信息：
  
  "13th Five Year Plan" Scientific Research Program of Jilin Provincial Department of Education
- DOI：10.37188/CJLCD.2020-0109
  CLC： TP391
- Received：28 July 2020，
  
  Accepted：16 October 2020，
  
  Published：2021-03
- 稿件说明：
移动端阅览
Ran-ran WEI, Wei-da ZHAN, De-peng ZHU, et al. Improved multi-scale Retinex infrared image enhancement[J]. Chinese journal of liquid crystals and displays, 2021, 36(3): 465-474.
DOI：

Ran-ran WEI, Wei-da ZHAN, De-peng ZHU, et al. Improved multi-scale Retinex infrared image enhancement[J]. Chinese journal of liquid crystals and displays, 2021, 36(3): 465-474. DOI： 10.37188/CJLCD.2020-0109.

摘要

为了解决传统的多尺度Retinex算法在对红外图像细节增强时产生光晕现象和增强图像噪声等问题，提出一种改进引导滤波器的多尺度Retinex的红外图像细节增强算法。首先，采用改进的引导滤波器代替高斯滤波器对红外图像进行入射分量的精确估计，并计算出其反射分量，以减少图像中的噪声，改善光晕现象。其次，对入射分量进行自适应性灰度拉伸处理，对反射分量采用较平缓的非线性曲线进行校正，以达到更好地增强红外图像总体亮度和对比度的目的。实验结果表明：本文算法与其他算法相比信息熵、峰值信噪比、平均梯度、标准差都有一定的改善，同时该算法所得到的红外增强图像的对比度更高，细节信息更丰富，人眼视觉效果更好。

Abstract

In order to solve the problems of halo phenomenon and image noise enhancement when the traditional multi-scale Retinex algorithm enhances the details of infrared images

an infrared image detail enhancement algorithm based on improved mult-iscale Retinex is proposed. Firstly

an improved guided filter is used instead of the Gaussian filter to accurately estimate the incident component of the infrared image and calculate its reflection component to reduce the noise in the image and improve the halo phenomenon. Secondly

an adaptive grayscale stretching process is performed on the incident component

and a smoother non-linear curve is used to correct the reflected component

so as to achieve the purpose of better enhancing the overall brightness and contrast of the infrared image. The experimental results show that compared with other corresponding algorithms

the information entropy

peak signal-to-noise ratio

average gradient

and standard deviation have been improved. At the same time

the infrared enhanced image obtained by this algorithm has higher contrast and clearer details. The human eyes have better visual effects.

关键词

Keywords

references

CAO Y P, YANG M Y, TISSE C L. Effective strip noise removal for low-textured infrared images based on 1-D guided filtering[J]. IEEE Transactions on Circuits and Systems for Video Technology , 2016, 26(12): 2176-2188.

LIU C W, SUI X B, LIU Y, et al . FPN estimation based nonuniformity correction for infrared imaging system[J]. Infrared Physics & Technology , 2019, 96(8): 22-29.

占必超, 吴一全, 纪守新. 基于平稳小波变换和Retinex的红外图像增强方法[J]. 光学学报, 2010, 30(10): 2788-2793.

ZHAN B C, WU Y Q, JI S X. Infrared image enhancement method based on stationary wavelet transformation and retinex[J]. Acta Optica Sinica , 2010, 30(10): 2788-2793. (in Chinese)

WANGA B, CHEN L L, LIU Y Z. New results on contrast enhancement for infrared images[J]. Optik , 2019, 178: 1264-1269.

郝宇, 王新赛, 张彦波, 等. 基于自适应尺度因子的Retinex红外图像增强算法[J]. 红外技术, 2016, 38(10): 855-859.

HAO Y, WANG X S, ZHANG Y B, et al . The infrared image enhancement algorithm based on adapted scale factor Retinex[J]. Infrared Technology , 2016, 38(10): 855-859. (in Chinese)

LAND E H, MCCANN J J. Lightness and retinex theory[J]. Journal of the Optical Society of America , 1971, 61(1): 1-11.

JOBSOND J, RAHMAN Z, WOODELL G A. Properties and performance of a center/surround Retinex[J]. IEEE Transactions on Image Processing , 1997, 6(3): 451-462.

JOBSOND J, RAHMAN Z, WOODELL G A. A multiscale Retinex for bridging the gap between color images and the human observation of scenes[J]. IEEE Transactions on Image Processing , 1997, 6(7): 965-976.

刘洋, 余建华, 顾志芹, 等. 一种改进型增强图像处理算法研究与应用[J]. 激光与红外, 2019, 49(3): 381-384.

LIU Y, YU J H, GU Z Q, et al . Research and application of an improved enhanced image processing algorithm[J]. Laser & Infrared , 2019, 49(3): 381-384. (in Chinese)

张承泓, 李范鸣, 吴滢跃. 基于自适应引导滤波的子带分解多尺度Retinex红外图像增强[J]. 红外技术, 2019, 41(4): 323-328.

ZHANG C H, LI F M, WU Y Y. Infrared image enhancement based on adaptive guided filter and sub-band decomposed multi-scale Retinex[J]. Infrared Technology , 2019, 41(4): 323-328. (in Chinese)

栾孟杰. 一种多分辨多尺度的红外图像增强算法[J]. 激光杂志, 2019, 40(8): 81-84.

LUAN M J. A multiresolution and multiscale infrared image enhancement algorithm[J]. Laser Journal , 2019, 40(8): 81-84. (in Chinese)

宋玉, 夏振平, 胡伏原, 等. 基于人因的液晶显示动态图像质量研究[J]. 液晶与显示, 2020, 35(10): 1051-1058.

SONG Y, XIA Z P, HU F Y, et al . Moving image quality of liquid crystal display based on human factor[J]. Chinese Journal of Liquid Crystals and Displays , 2020, 35(10): 1051-1058. (in Chinese)

ASHIBAM I, TOLBA M S, EL-FISHAWY A S, et al . Gamma correction enhancement of infrared night vision images using histogram processing[J]. Multimedia Tools and Applications , 2019, 78(19): 27771-27783.

赵晨, 吕国强, 吴磊, 等. 基于图像局部亮度的液晶显示器动态调光算法[J]. 液晶与显示, 2020, 35(3): 234-241.

ZHAO C, LV G Q, WU L, et al . Dynamic dimming algorithm for liquid crystal display based on local brightness of image[J]. Chinese Journal of Liquid Crystals and Displays , 2020, 35(3): 234-241. (in Chinese)

程亚亚, 于化东, 于占江, 等. 微铣刀同轴全息图像增强方法[J]. 中国光学, 2020, 13(4): 705-712.

CHENG Y Y, YU H D, YU Z J, et al . Method of enhancing the quality of in-line holographic images for micro-milling tool[J]. Chinese Optics , 2020, 13(4): 705-712. (in Chinese)

Views

106

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Adaptive guided filtering based infrared image detail enhancement

Image defogging algorithm based on improved dark channel and adaptive tolerance

Fast color transfer algorithm based on neighborhood-first searching

Related Author

Yong TIAN

De-peng ZHU

Ran-ran WEI

Wei-da ZHAN

WANG Jia-rong

HAO Zhi-cheng

ZHU Ming

YANG Jin-cheng

Related Institution

Changchun University of Science and Technology， Department of Electronic Information Engineering

Changchun Institute of Optics， Fine Mechanics and Physics， Chinese Academy of Sciences

Chongqing JiaLing HuaGuang Photoelectric Technology Co.， Ltd.

School of Network and Communications， Hebei Polytechnic Institute

College ofartificial intelligence，Yango University

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China 130033 Postal code：130033
Tel：0431-86176059 Email：yjxs@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰