Feature Enhancement Derivative Fusion Algorithm Based on Luminance Evaluation Technology
CSTR:
Author:
  • Article
  • | |
  • Metrics
  • |
  • Reference [20]
  • |
  • Related [20]
  • | | |
  • Comments
    Abstract:

    Focused on the low-light images obtained from dynamic range, illumination condition, image acquisition equipment, etc., a feature enhancement derivative fusion algorithm based on luminance evaluation technology was proposed to achieve contrast adjustment and feature enhancement of the low-light images. Firstly, the brightness evaluation technique was used to optimize the brightness of the low-light image to obtain the exposure ratio map. Then, combining exposure ratio map and improved chi-square distribution function model, two derivatives with enhanced features were obtained for fusion. Finally, the fusion image was obtained by using the improved derivative fusion algorithm. The experimental results indicate that the proposed algorithm achieves the better results including brightness order error, visual information fidelity and image mutual information, improves the image contrast while preserving the well-exposed region, and it can recover the edge and texture details of the low-luminance region.

    Reference
    [1] 刘栋, 周冬明, 聂仁灿, 等. NSCT域内结合相位一致性激励PCNN的多聚焦图像融合. 计算机应用, 2018, 38(10):3006-3012.[doi:10.11772/j.issn.1001-9081.2018040885
    [2] Parihar AS, Singh K. A study on Retinex based method for image enhancement. 2018 2nd International Conference on Inventive Systems and Control. Coimbatore, India. 2018. 619-624.
    [3] Brainard DH, Wandell BA. Analysis of the Retinex theory of color vision. Journal of the Optical Society of America A, 1986, 3(10):1651-1661.[doi:10.1364/josaa.3.001651
    [4] Jobson DJ, Rahman Z, Woodell GA. A multiscale Retinex for bridging the gap between color images and the human observation of scenes. IEEE Transactions on Image Processing, 1997, 6(7):965-976.[doi:10.1109/83.597272
    [5] Petro AB, Sbert C, Morel JM. Multiscale Retinex. Image Processing on Line, 2014, 4:71-88.[doi:10.5201/ipol.2014.107
    [6] Dong X, Pang Y, Wen J. Fast efficient algorithm for enhancement of low lighting video. ACM SIGGRAPH 2010 Posters. Los Angeles, CA, USA. 2010. 1-6.
    [7] Fu XY, Zeng DL, Huang Y, et al. A weighted variational model for simultaneous reflectance and illumination estimation. 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA. 2016. 2782-2790.
    [8] Wang SH, Zheng J, Hu HM, et al. Naturalness preserved enhancement algorithm for non-uniform illumination images. IEEE Transactions on Image Processing, 2013, 22(9):3538-3548.[doi:10.1109/TIP.2013.2261309
    [9] Guo XJ, Li Y, Ling HB. LIME:Low-light image enhancement via illumination map estimation. IEEE Transactions on Image Processing, 2017, 26(2):982-993.[doi:10.1109/TIP.2016.2639450
    [10] Fu XY, Zeng DL, Huang Y, et al. A fusion-based enhancing method for weakly illuminated images. Signal Processing, 2016, 129:82-96.[doi:10.1016/j.sigpro.2016.05.031
    [11] 华顺刚, 王丽丹, 欧宗瑛. 基于多幅不同曝光量照片的场景高动态范围图像合成. 大连理工大学学报, 2007, 47(5):678-682
    [12] Land EH. The Retinex theory of color vision. Scientific American, 1977, 237(6):108-128.[doi:10.1038/scientificamerican1277-108
    [13] 熊昌镇, 车满强, 王润玲. 基于稀疏卷积特征和相关滤波的实时视觉跟踪算法. 计算机应用, 2018, 38(8):2175-2179
    [14] 杨兴东, 邵保刚, 吴亚娟. 矩阵Frobenius范数不等式. 高等学校计算数学学报, 2009, 31(1):42-49.[doi:10.3969/j.issn.1000-081X.2009.01.005
    [15] Mei Z, Kai J, Wang S, et al. Color retinal image enhancement based on luminosity and contrast adjustment. IEEE Transactions on Biomedical Engineering, 2018, 65(3):521-527.[doi:10.1109/TBME.2017.2700627
    [16] 陈刚, 王梦婕. 卡方分布密度函数与分布函数的渐近展开. 南京师大学报(自然科学版), 2014, 37(3):39-43.[doi:10.3969/j.issn.1001-4616.2014.03.007
    [17] 江铁, 朱桂斌, 孙奥. 基于金字塔变换的多曝光图像融合. 计算机技术与发展, 2013, 23(1):95-98
    [18] Wang H, Gao CH, Xie XX, et al. Study on edge detection method of aluminum foil image. 2017 International Conference on Computer Systems, Electronics and Control. Dalian, China. 2017. 1008-1010.
    [19] 王琳, 毕笃彦, 李晓辉, 等. 基于负修正和对比度拉伸的快速去雾算法. 计算机应用, 2016, 36(4):1106-1110.[doi:10.3969/j.issn.1001-3695.2016.04.032
    [20] 刘军, 白雪. 基于梯度方向直方图与高斯金字塔的车牌模糊汉字识别方法. 计算机应用, 2016, 36(2):586-590.[doi:10.3969/j.issn.1001-3695.2016.02.061
    Cited by
    Comments
    Comments
    分享到微博
    Submit
Get Citation

韦超,唐丽娟,陈冠楠.基于亮度评估技术的特征增强衍生图融合算法.计算机系统应用,2019,28(11):195-201

Copy
Share
Article Metrics
  • Abstract:1488
  • PDF: 2538
  • HTML: 2039
  • Cited by: 0
History
  • Received:April 19,2019
  • Revised:May 16,2019
  • Online: November 08,2019
  • Published: November 15,2019
Article QR Code
You are the first990394Visitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-3
Address:4# South Fourth Street, Zhongguancun,Haidian, Beijing,Postal Code:100190
Phone:010-62661041 Fax: Email:csa (a) iscas.ac.cn
Technical Support:Beijing Qinyun Technology Development Co., Ltd.

Beijing Public Network Security No. 11040202500063