Low-light image enhancement method based on simulating multi-exposure fusion

doi:10.11772/j.issn.1001-9081.2018112284

Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (6): 1804-1809.DOI: 10.11772/j.issn.1001-9081.2018112284

• Virtual reality and multimedia computing • Previous Articles Next Articles

Low-light image enhancement method based on simulating multi-exposure fusion

SIMA Ziling^1,2, HU Feng^1,2

1. College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
2. Chongqing Key Laboratory of Computational Intelligence(Chongqing University of Posts and Telecommunications), Chongqing 400065, China

Received:2018-11-19 Revised:2019-01-18 Online:2019-06-17 Published:2019-06-10
Supported by:
This work is partially supported by the National Key Research and Development Program of China (2018YFC0808305), the National Natural Science Foundation of China (61751312, 61533020, 61309014), the Chongqing Special Fund for Common Key Technology Innovations in Key Industries (cstc2017zdcy-zdyfX0001, cstc2017zdcy-zdzx0046), the Chongqing Research Program of Basic Research and Frontier Technology (cstc2017jcyjAX0408).

基于模拟多曝光融合的低照度图像增强方法

司马紫菱^1,2, 胡峰^1,2

1. 重庆邮电大学计算机科学与技术学院, 重庆, 400065;
2. 计算智能重庆市重点实验室(重庆邮电大学), 重庆, 400065

通讯作者: 司马紫菱
作者简介:司马紫菱(1996-),女,湖北天门人,硕士研究生,主要研究方向:智能信息处理;胡峰(1978-),男,湖北天门人,教授,博士,主要研究方向:数据挖掘、Rough集、粒计算、智能信息处理。
基金资助:
国家重点研发计划项目（2018YFC0808305）；国家自然科学基金资助项目（61751312，61533020，61309014）；重庆市重点产业共性关键技术创新专项（cstc2017zdcy-zdyfX0001，cstc2017zdcy-zdzx0046）；重庆市基础科学与前沿技术研究专项（cstc2017jcyjAX0408）。

Abstract

Abstract: Aiming at the problems of low luminance, low contrast and poor visual information, a low-light image enhancement method based on simulating multi-exposure fusion was proposed. Firstly, the improved variational Retinex model and morphology were combined to generate the reference map to ensure the subject information in the exposed image set. Then, a new illumination compensation normalization function was constructed by combining Sigmoid function and gamma correction. At the same time, an unsharp masking algorithm based on Gaussian guided filtering was proposed to adjust the details of the reference map. Finally, the weighted values of exposed image set were designed from luminance, chromatic information and exposure rate respectively, and the final enhancement result was obtained through multi-scale fusion with effective avoidance of halo phenomenon and color distortion. The experimental results on different public datasets show that, compared with the traditional low-light image enhancement method, the proposed method has reduced the lightness distortion rate and increased the visual information fidelity. The proposed method can effectively preserve visual information, which is conducive to the real-time application of low-light image enhancement.

Key words: low-light image, Retinex theory, exposure fusion, detail adjustment, image enhancement

摘要： 针对部分低照度图像整体亮度偏暗、对比度差和视觉信息偏弱等问题，提出一种基于模拟多曝光融合的低照度图像增强方法。首先，利用改进的变分Retinex模型和形态学的结合产生基准图来保证曝光图像集中的主体信息；其次，结合Sigmoid函数和伽马矫正构造新的光照补偿归一化函数，同时提出了一种基于高斯引导滤波的反锐化掩模算法，用于调整基准图的细节；最后，分别从亮度、色调和曝光率设计曝光图集的加权值，通过多尺度融合得到最终增强结果，有效地避免了增强结果中的光晕和颜色失真。在不同的公开数据集上的实验结果表明，与传统的低照度图像增强方法进行相比，所提方法降低了亮度失真率，提升了视觉信息保真度。该方法能够有效地保留视觉信息，有利于实现低照度图像增强的实时性应用。

关键词: 低照度图像, Retinex理论, 曝光融合, 细节调整, 图像增强

CLC Number:

TP391.41

SIMA Ziling, HU Feng. Low-light image enhancement method based on simulating multi-exposure fusion[J]. Journal of Computer Applications, 2019, 39(6): 1804-1809.

司马紫菱, 胡峰. 基于模拟多曝光融合的低照度图像增强方法[J]. 计算机应用, 2019, 39(6): 1804-1809.

References

[1] YUE H J, YANG J Y, SUN X Y, et al. Contrast enhancement based on intrinsic image decomposition[J]. IEEE Transactions on Image Processing, 2017, 26(8):3981-3994.
[2] LEE C, LEE C, KIM C S. Contrast enhancement based on layered difference representation of 2D histograms[J]. IEEE Transactions on Image Processing, 2013, 22(12):5372-5384.
[3] CELIK T, TJAHJADI T. Contextual and variational contrast enhancement[J]. IEEE Transactions on Image Processing, 2011, 20(12):3431-3441.
[4] GU K, ZHAI G T, LIN W S, et al. The analysis of image contrast:from quality assessment to automatic enhancement[J]. IEEE Transactions on Cybernetics, 2016, 46(1):284-297.
[5] JOBSON D 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.
[6] 刘茜,卢心红,李象霖.基于多尺度Retinex的自适应图像增强方法[J].计算机应用,2009,29(8):2077-2079.(LIU Q, LU X H, LI X L. Adaptive image enhancement method based on multi-scale Retinex algorithm[J].Journal of Computer Applications, 2009, 29(8):2077-2079.)
[7] WANG S H, ZHENG J, HU H M, et al. Naturalness preserved enhancement algorithm for non-uniform illumination images[J]. IEEE Transactions on Image Processing, 2013, 22(9):3538-3548.
[8] 王小明,黄昶,李全彬,等.改进的多尺度Retinex图像增强算法[J].计算机应用,2010,30(8):2091-2093.(WANG X M, HUANG C, LI Q B, et al. Improved multi-scale Retinex image enhancement algorithm[J]. Journal of Computer Applications, 2010, 30(8):2091-2093.)
[9] FU X Y, LIAO Y H, ZENG D L, et al. A probabilistic method for image enhancement with simultaneous illumination and reflectance estimation[J]. IEEE Transactions on Image Processing, 2015, 24(12):4965-4977.
[10] WANG Y F, WANG H Y, YIN C L, et al. Biologically inspired image enhancement based on Retinex[J]. Neurocomputing, 2016, 177(C):373-384.
[11] GUO X J, LI Y, LING H B. LIME:low-light image enhancement via illumination map estimation[J]. IEEE Transactions on Image Processing, 2017, 26(2):982-993.
[12] FU X Y, ZENG D L, HUANG Y, et al. A weighted variational model for simultaneous reflectance and illumination estimation[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2016:2782-2790.
[13] KIMMEL R, ELAD M, SHAKED D, et al. A variational framework for Retinex[J]. International Journal of Computer Vision, 2003, 52(1):7-23.
[14] GOLDSTEIN T, OSHER S. The split bregman method for L1-regularized problems[J]. SIAM Journal on Imaging Sciences, 2009, 2(2):323-343.
[15] DENG G. A generalized unsharp masking algorithm[J]. IEEE Transactions on Image Processing, 2011, 20(5):1249-1261.
[16] ANCUTI C O, ANCUTI C. Single image dehazing by multi-scale fusion[J]. IEEE Transactions on Image Processing, 2013, 22(8):3271- 3282.
[17] ANCUTI C O, ANCUTI C, de VLEESCHOUWER C, et al. Single-scale fusion:an effective approach to merging images[J]. IEEE Transactions on Image Processing, 2017, 26(1):65-78.
[18] ANCUTI C, ANCUTI C O, HABER T, et al. Enhancing underwater images and videos by fusion[C]//Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2012:81-88.
[19] ANCUTI C O, ANCUTI C, BEKAERT P. Enhancing by saliency-guided decolorization[C]//CVPR 2011:Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2011:257-264.
[20] CHOI L K, YOU J, BOVIK A C. Referenceless prediction of perceptual fog density and perceptual image defogging[J]. IEEE Transactions on Image Processing, 2015, 24(11):3888-3901.
[21] JOBSON D 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.
[22] XU H T, ZHAI G Z, WU X L, et al. Generalized equalization model for image enhancement[J]. IEEE Transactions on Multimedia, 2014, 16(1):68-82.
[23] SHAN Q, JIA J Y, BROWN M S. Globally optimized linear windowed tone mapping[J]. IEEE transactions on Visualization and Computer Graphics, 2010, 16(4):663-675.
[24] YING Z Q, LI G, REN Y R, et al. A new image contrast enhancement algorithm using exposure fusion framework[C]//CAIP 2017:Proceedings of the 2017 International Conference on Computer Analysis of Images and Patterns, LNCS 10425. Cham:Springer, 2017:36-46.
[25] 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.
[26] MA K, ZENG K, WANG Z. Perceptual quality assessment for multi-exposure image fusion[J]. IEEE Transactions on Image Processing, 2015, 24(11):3345- 3356.
[27] SHEIKH H R, BOVIK A C. Image information and visual quality[J]. IEEE Transactions on Image Processing, 2006, 15(2):430-444.
[28] LI Z G, ZHENG J H, ZHU Z J, et al. Weighted guided image filtering[J]. IEEE Transactions on Image Processing, 2015, 24(1):120-129.
[29] AYDIN T O, MANTIUK R, MYSZKOWSKI K, et al. Dynamic range independent image quality assessment[C]//SIGGRAPH 2008:Proceedings of the 2008 ACM SIGGRAPH. New York:ACM, 2008:Article No. 69.
[30] WANG Q H, FU X Y, ZHANG X P, et al. A fusion-based method for single backlit image enhancement[C]//ICIP 2016:Proceedings of the 2016 IEEE International Conference on Image Processing. Piscataway, NJ:IEEE, 2016:4077-4081.
[31] YING Z Q, LI G, REN Y R, et al. A new low-light image enhancement algorithm using camera response model[C]//Proceedings of the 2017 IEEE International Conference on Computer Vision Workshop. Piscataway, NJ:IEEE, 2017:3015-3022.
[32] 王晨,汤心溢,高思莉.基于人眼视觉的红外图像增强算法研究[J].激光与红外,2017,47(1):114-118.(WANG C, TANG X Y, GAO S L. Infrared image enhancement algorithm based on human vision[J]. Laser and Infrared, 2017, 47(1):114-118.)

Low-light image enhancement method based on simulating multi-exposure fusion

基于模拟多曝光融合的低照度图像增强方法

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