Search

Article

x

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

Advanced Retinex-Net image enhancement method based on value component processing

Zhang Hang-Ying Wang Xue-Qi Wang Hua-Ying Cao Liang-Cai

Citation:

Advanced Retinex-Net image enhancement method based on value component processing

Zhang Hang-Ying, Wang Xue-Qi, Wang Hua-Ying, Cao Liang-Cai
PDF
HTML
Get Citation
  • When capturing images under low-light lighting conditions, the resulting images often suffer low visibility. Such low-visibility images not only affect the visual effect but also cause many difficulties in practical application. Therefore, image enhancement technology under low-light conditions has always been a challenging problem in image algorithms. Considering that most of the existing image enhancement methods are based on the RGB color space enhancement technology, the correlation among the RGB three primary colors is ignored, which makes the color distortion phenomenon easy to occur when the image is enhanced. To solve the problems of poor image visibility and color deviation under low-light conditions, in this paper an advanced Retinex network enhancement method is proposed. In the method, firstly the low-light RGB image is transformed into HSV color space, the Retinex decomposition network is used to decompose and enhance the value component separately, and thus increasing the resolution of the value component through up-sampling operation; then, for the hue component and saturation component, the nearest neighbor interpolation is used to increase their resolutions, and the enhanced value component is combined to convert back to RGB color space to obtain the initial enhanced image; finally, the wavelet transform image fusion technology is used to fuse with the original low-light image to eliminate the over-enhanced part in the initial enhanced image. The analysis of experimental results shows that the method proposed in this paper has obvious advantages in brightness enhancement and color restoration of low-light images. Especially, comparing with the original Retinex network method, the NIQE value decreases by an average of 19.49%, and the image standard deviation increases by an average of 41.35%. The algorithm proposed in this paper is expected to be effectively used in the fields of security monitoring and biomedicine.
      Corresponding author: Cao Liang-Cai, clc@tsinghua.edu.cn
    • Funds: Project supported by the National Natural Science Foundation of China (Grant No. 61827825 ).
    [1]

    蒋一纯, 詹伟达, 朱德鹏 2021 激光与光电子学进展 58 0410001Google Scholar

    Jiang Y C, Zhan W D, Zhu D P 2021 Laser Optoelectron. Prog. 58 0410001Google Scholar

    [2]

    韩平丽, 刘飞, 张广, 陶禹, 邵晓鹏 2018 物理学报 67 054202Google Scholar

    Han P L, Liu F, Zhang G, Tao Y, Shao X P 2018 Acta Phys. Sin. 67 054202Google Scholar

    [3]

    Liu J, Wang X, Chen M, Liu S G, Zhou X R, Shao Z F, Liu P 2014 Opt. Express 22 618Google Scholar

    [4]

    Fu X Y, Zeng D L, Huang Y, Liao Y H, Ding X H, Paisley J 2016 Signal Process. 129 82Google Scholar

    [5]

    Singh N, Bhandari A K 2021 IEEE Trans. Instrum. Meas. 70 1

    [6]

    Land E H 1964 Am. Sci. 52 247

    [7]

    Land E H, McCann J J 1971 J. Opt. Soc. Am. 61 1Google Scholar

    [8]

    Land E H, Hubel D H, Livingstone M S, Perry S H, Burns M M 1983 Nature 303 616Google Scholar

    [9]

    李红, 吴炜, 杨晓敏, 严斌宇, 刘凯, Gwanggil J 2016 物理学报 65 160701Google Scholar

    Li H, Wu W, Yang X M, Yan B Y, Liu K, Gwanggil J 2016 Acta Phys. Sin. 65 160701Google Scholar

    [10]

    Jobson D J, Rahman Z, Woodell G A 1997 IEEE Trans. Image Process. 6 451Google Scholar

    [11]

    Rahman Z, Jobson D J, Woodell G A 1996 Proceedings of 3rd IEEE International Conference on Image Processing Lausanne, Switzerland, September 19, 1996 p1003

    [12]

    Jobson D J, Rahman Z, Woodell G A 1997 IEEE Trans. Image Process. 6 965Google Scholar

    [13]

    毕国玲, 续志军, 赵建, 孙强 2015 物理学报 64 100701Google Scholar

    Bi G L, Xu Z J, Zhao J, Sun Q 2015 Acta Phys. Sin. 64 100701Google Scholar

    [14]

    Zhou Z Q, Dong M J, Xie X Z, Gao Z F 2016 Appl. Opt. 55 6480

    [15]

    王殿伟, 韩鹏飞, 范九 伦, 刘颖, 许志杰, 王晶 2018 物理学报 67 210701Google Scholar

    Wang D W, Han P F, Fan J L, Liu Y, Xu Z J, Wang J 2018 Acta Phys. Sin. 67 210701Google Scholar

    [16]

    Kwon H J, Lee S H, Lee G Y, Sohng K I 2014 Digit. Signal Process. 30 74Google Scholar

    [17]

    Yang Q X, Tan K H, Ahuja N 2009 IEEE Conference on Computer Vision and Pattern Recognition Miami, USA, June 20–25, 2009 p557

    [18]

    Wang S H, Zheng J, Hu H M, Li B 2013 IEEE Trans. Image Process. 22 3538Google Scholar

    [19]

    Fu X Y, Zeng D L, Huang Y, Zhang X P, Ding X H 2016 IEEE Conference on Computer Vision and Pattern Recognition Las Vegas, USA, June 27–30, 2016 p2782

    [20]

    Guo X J, Li Y, Ling H B 2017 IEEE Trans. Image Process. 26 982Google Scholar

    [21]

    Gijsenij A, Gevers T, Weijer J 2011 IEEE Trans. Image Process. 20 2475Google Scholar

    [22]

    赵欣慰, 金韬, 池灏, 曲嵩 2015 物理学报 64 104201Google Scholar

    Zhao X W, Jin T, Chi H, Qu S 2015 Acta Phys. Sin. 64 104201Google Scholar

    [23]

    Jiang Z Q, Li H T, Liu L j, Men A D, Wang H Y 2021 Neurocomputing 454 361Google Scholar

    [24]

    马红强, 马时平, 许悦雷, 朱明明 2019 光学学报 39 0210004Google Scholar

    Ma H Q, Ma S P, Xu Y L, Zhu M M 2019 Acta Opt. Sin. 39 0210004Google Scholar

    [25]

    Guo Y H, Ke X, Ma J, Zhang J 2019 IEEE Access 7 13737Google Scholar

    [26]

    Lore K G, Akintayo A, Sarkar S 2017 Pattern Recognit. 61 650Google Scholar

    [27]

    Wang W J, Wei C, Yang W H, Liu J Y 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition Xi'an, China, May 15–19, 2018 p751

    [28]

    He W J, Liu Y Y, Feng J F, Zhang W W, Gu G H, Chen Q 2020 IEEE 3rd International Conference on Information Systems and Computer Aided Education Dalian, China, September 27–29, 2020 p397

    [29]

    Wei C, Wang W J, Yang W H, Liu J Y 2018 arXiv: 1808.04560 v1 [cs. CV]

    [30]

    Yakno M, Mohamad-Saleh J, Ibrahim M Z 2021 Sensors 21 6445Google Scholar

    [31]

    陈刚, 刘言, 杨贺超, 孙斌, 喻春雨 2021 光学精密工程 29 1999Google Scholar

    Chen G, Liu Y, Yang H C, Sun B, Yu C Y 2021 Opt. Precis. Eng. 29 1999Google Scholar

    [32]

    Zhang H Y, Cao L C, Yang F 2021 Proc. SPIE First Optics Frontier Conference Hangzhou, China, June 18, 2021 1185002

    [33]

    Yadav A K, Roy R, Kumar A P, Kumar C S, Dhakad S K 2015 International Conference on Advances in Computing, Communications and Informatics Kochi, India, August 10–13, 2015 p1204

    [34]

    Mittal A, Soundararajan R, Bovik A C 2013 IEEE Signal Process. Lett. 20 209Google Scholar

  • 图 1  (a) Retinex成像理论模型; (b) Retinex算术思想简介

    Figure 1.  (a) Retinex imaging theoretical model; (b) arithmetic ideas of Retinex algorithm.

    图 2  改进的Retinex网络增强算法流程图

    Figure 2.  Flow chart of the advanced Retinex network enhancement algorithm.

    图 3  sym4尺度函数与小波函数

    Figure 3.  Scale function and wavelet function of sym4.

    图 4  不同算法对比效果图

    Figure 4.  Comparison of different algorithms.

    图 5  不同算法增强效果局部放大图

    Figure 5.  Local enlarged view of the enhancement effect of different algorithms.

    图 6  不同方法图像评价指标的均值变化情况

    Figure 6.  Changes in mean values of image evaluation metrics for different methods.

    表 1  V分量分解网络结构

    Table 1.  V-component decomposition network structure.

    输入操作卷积核输出通道步长输出
    RGBrgb to hsvH, S, V
    Vconv3641feats0
    feats0conv & ReLU3641feats1
    feats1conv & ReLU3641feats2
    feats2conv & ReLU3641feats3
    feats3conv & ReLU3641feats4
    feats4conv & ReLU3641feats5
    feats5conv & sigmoid321R, I
    DownLoad: CSV

    表 2  V分量增强网络结构

    Table 2.  V-component enhancement network structure.

    输入操作卷积核输出通道步长输出
    Vlow, Rlow, Ilowup-sampleInput
    Inputconv3641out0
    out0conv & ReLU3642out1
    out1conv & ReLU3642out2
    out2conv & ReLU3642out3
    out3interpolation64out3 up
    out3 up, out2
    de1
    conv & ReLU
    interpolation
    3
    64
    64
    1
    de1
    de1 up
    de1 up, out1
    de2
    conv & ReLU
    interpolation
    3
    64
    64
    1
    de2
    de2 up
    de2 up, out0de1
    de2
    conv & ReLUinterpolation
    interpolation
    3—
    6464
    64
    1—
    de3de1 r
    de2 r
    de1 r, de2 r, de3conv & ReLU3641feats0
    feats0conv1641feats1
    feats1conv311Vnew
    DownLoad: CSV

    表 3  不同图像的客观评价指标

    Table 3.  Objective evaluation metrics for different images.

    ImageEvaluateMSRCRAuto GCRetinex-NetARN
    Image1NIQE5.66925.13845.97824.0729
    Entropy7.10956.63927.13757.8179
    SD33.375841.695931.113042.9601
    Image 2NIQE6.29266.02525.35963.7336
    Entropy7.30127.51717.57777.7226
    SD41.890355.607146.342866.2911
    Image 3NIQE5.67154.92034.45284.0319
    Entropy6.78987.12247.72847.8633
    SD31.380037.302853.565472.4424
    Image 4NIQE3.76953.88443.72003.6582
    Entropy5.53927.18817.28077.4010
    SD40.891738.674139.991346.9674
    Image 5NIQE3.95414.47384.01263.6424
    Entropy7.34976.05497.28717.4387
    SD42.157441.086332.532156.5474
    Image 6NIQE7.34016.42735.44593.8790
    Entropy7.03355.57017.34177.8134
    SD34.113640.610838.497456.5800
    MeanNIQE5.44955.14494.76493.8363
    Entropy6.85386.68207.39227.6762
    SD37.301542.496240.300356.9647
    DownLoad: CSV
  • [1]

    蒋一纯, 詹伟达, 朱德鹏 2021 激光与光电子学进展 58 0410001Google Scholar

    Jiang Y C, Zhan W D, Zhu D P 2021 Laser Optoelectron. Prog. 58 0410001Google Scholar

    [2]

    韩平丽, 刘飞, 张广, 陶禹, 邵晓鹏 2018 物理学报 67 054202Google Scholar

    Han P L, Liu F, Zhang G, Tao Y, Shao X P 2018 Acta Phys. Sin. 67 054202Google Scholar

    [3]

    Liu J, Wang X, Chen M, Liu S G, Zhou X R, Shao Z F, Liu P 2014 Opt. Express 22 618Google Scholar

    [4]

    Fu X Y, Zeng D L, Huang Y, Liao Y H, Ding X H, Paisley J 2016 Signal Process. 129 82Google Scholar

    [5]

    Singh N, Bhandari A K 2021 IEEE Trans. Instrum. Meas. 70 1

    [6]

    Land E H 1964 Am. Sci. 52 247

    [7]

    Land E H, McCann J J 1971 J. Opt. Soc. Am. 61 1Google Scholar

    [8]

    Land E H, Hubel D H, Livingstone M S, Perry S H, Burns M M 1983 Nature 303 616Google Scholar

    [9]

    李红, 吴炜, 杨晓敏, 严斌宇, 刘凯, Gwanggil J 2016 物理学报 65 160701Google Scholar

    Li H, Wu W, Yang X M, Yan B Y, Liu K, Gwanggil J 2016 Acta Phys. Sin. 65 160701Google Scholar

    [10]

    Jobson D J, Rahman Z, Woodell G A 1997 IEEE Trans. Image Process. 6 451Google Scholar

    [11]

    Rahman Z, Jobson D J, Woodell G A 1996 Proceedings of 3rd IEEE International Conference on Image Processing Lausanne, Switzerland, September 19, 1996 p1003

    [12]

    Jobson D J, Rahman Z, Woodell G A 1997 IEEE Trans. Image Process. 6 965Google Scholar

    [13]

    毕国玲, 续志军, 赵建, 孙强 2015 物理学报 64 100701Google Scholar

    Bi G L, Xu Z J, Zhao J, Sun Q 2015 Acta Phys. Sin. 64 100701Google Scholar

    [14]

    Zhou Z Q, Dong M J, Xie X Z, Gao Z F 2016 Appl. Opt. 55 6480

    [15]

    王殿伟, 韩鹏飞, 范九 伦, 刘颖, 许志杰, 王晶 2018 物理学报 67 210701Google Scholar

    Wang D W, Han P F, Fan J L, Liu Y, Xu Z J, Wang J 2018 Acta Phys. Sin. 67 210701Google Scholar

    [16]

    Kwon H J, Lee S H, Lee G Y, Sohng K I 2014 Digit. Signal Process. 30 74Google Scholar

    [17]

    Yang Q X, Tan K H, Ahuja N 2009 IEEE Conference on Computer Vision and Pattern Recognition Miami, USA, June 20–25, 2009 p557

    [18]

    Wang S H, Zheng J, Hu H M, Li B 2013 IEEE Trans. Image Process. 22 3538Google Scholar

    [19]

    Fu X Y, Zeng D L, Huang Y, Zhang X P, Ding X H 2016 IEEE Conference on Computer Vision and Pattern Recognition Las Vegas, USA, June 27–30, 2016 p2782

    [20]

    Guo X J, Li Y, Ling H B 2017 IEEE Trans. Image Process. 26 982Google Scholar

    [21]

    Gijsenij A, Gevers T, Weijer J 2011 IEEE Trans. Image Process. 20 2475Google Scholar

    [22]

    赵欣慰, 金韬, 池灏, 曲嵩 2015 物理学报 64 104201Google Scholar

    Zhao X W, Jin T, Chi H, Qu S 2015 Acta Phys. Sin. 64 104201Google Scholar

    [23]

    Jiang Z Q, Li H T, Liu L j, Men A D, Wang H Y 2021 Neurocomputing 454 361Google Scholar

    [24]

    马红强, 马时平, 许悦雷, 朱明明 2019 光学学报 39 0210004Google Scholar

    Ma H Q, Ma S P, Xu Y L, Zhu M M 2019 Acta Opt. Sin. 39 0210004Google Scholar

    [25]

    Guo Y H, Ke X, Ma J, Zhang J 2019 IEEE Access 7 13737Google Scholar

    [26]

    Lore K G, Akintayo A, Sarkar S 2017 Pattern Recognit. 61 650Google Scholar

    [27]

    Wang W J, Wei C, Yang W H, Liu J Y 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition Xi'an, China, May 15–19, 2018 p751

    [28]

    He W J, Liu Y Y, Feng J F, Zhang W W, Gu G H, Chen Q 2020 IEEE 3rd International Conference on Information Systems and Computer Aided Education Dalian, China, September 27–29, 2020 p397

    [29]

    Wei C, Wang W J, Yang W H, Liu J Y 2018 arXiv: 1808.04560 v1 [cs. CV]

    [30]

    Yakno M, Mohamad-Saleh J, Ibrahim M Z 2021 Sensors 21 6445Google Scholar

    [31]

    陈刚, 刘言, 杨贺超, 孙斌, 喻春雨 2021 光学精密工程 29 1999Google Scholar

    Chen G, Liu Y, Yang H C, Sun B, Yu C Y 2021 Opt. Precis. Eng. 29 1999Google Scholar

    [32]

    Zhang H Y, Cao L C, Yang F 2021 Proc. SPIE First Optics Frontier Conference Hangzhou, China, June 18, 2021 1185002

    [33]

    Yadav A K, Roy R, Kumar A P, Kumar C S, Dhakad S K 2015 International Conference on Advances in Computing, Communications and Informatics Kochi, India, August 10–13, 2015 p1204

    [34]

    Mittal A, Soundararajan R, Bovik A C 2013 IEEE Signal Process. Lett. 20 209Google Scholar

  • [1] Liu Hong-Jiang, Liu Yi-Fei, Gu Fu-Xing. Automatic fabrication system of optical micro-/nanofiber based on deep learning*. Acta Physica Sinica, 2024, 0(0): 0-0. doi: 10.7498/aps.73.20240171
    [2] Zhan Qing-Liang, Ge Yao-Jun, Bai Chun-Jin. Flow feature extraction models based on deep learning. Acta Physica Sinica, 2022, 71(7): 074701. doi: 10.7498/aps.71.20211373
    [3] Xu Zhao, Zhou Xin, Bai Xing, Li Cong, Chen Jie, Ni Yang. Attacking asymmetric cryptosystem based on phase truncated Fourier fransform by deep learning. Acta Physica Sinica, 2021, 70(14): 144202. doi: 10.7498/aps.70.20202075
    [4] Zhang Yao, Zhang Yun-Bo, Chen Li. Deep-learning-assisted micro impurity detection on an optical surface. Acta Physica Sinica, 2021, 70(16): 168702. doi: 10.7498/aps.70.20210403
    [5] Wang Tian-Tian, Wang Hui, Zhu Yan-Chun, Wang Li-Jia. Motion tracking of left myocardium in cardiac cine magnetic resonance image based on displacement flow U-Net and variational autoencoder. Acta Physica Sinica, 2021, 70(22): 228701. doi: 10.7498/aps.70.20210885
    [6] Nan Hu, Ma Xiao-Jing, Zhao Hai-Bo, Tang Shao-Jie, Liu Wei-Hua, Wang Da-Wei, Jia Chun-Lin. Detection of intensity peaks in high-resolution transmission electron microscopy image based on YOLOv3. Acta Physica Sinica, 2021, 70(7): 076803. doi: 10.7498/aps.70.20201502
    [7] Su Bo, Tao Fen, Li Ke, Du Guo-Hao, Zhang Ling, Li Zhong-Liang, Deng Biao, Xie Hong-Lan, Xiao Ti-Qiao. Image alignment for synchrotron radiation based X-ray nano-CT. Acta Physica Sinica, 2021, 70(16): 160704. doi: 10.7498/aps.70.20210156
    [8] Zhou Jing, Zhang Xiao-Fang, Zhao Yan-Geng. Phase retrieval wavefront sensing based on image fusion and convolutional neural network. Acta Physica Sinica, 2021, 70(5): 054201. doi: 10.7498/aps.70.20201362
    [9] Zhao Zhi-Peng, Zhou Shuang, Wang Xing-Yuan. A new chaotic signal based on deep learning and its application in image encryption. Acta Physica Sinica, 2021, 70(23): 230502. doi: 10.7498/aps.70.20210561
    [10] Lang Li-Ying, Lu Jia-Lei, Yu Na-Na, Xi Si-Xing, Wang Xue-Guang, Zhang Lei, Jiao Xiao-Xue. In depth learning based method of denoising joint transform correlator optical image encryption system. Acta Physica Sinica, 2020, 69(24): 244204. doi: 10.7498/aps.69.20200805
    [11] Chen Wei, Guo Yuan, Jing Shi-Wei. General image encryption algorithm based on deep learning compressed sensing and compound chaotic system. Acta Physica Sinica, 2020, 69(24): 240502. doi: 10.7498/aps.69.20201019
    [12] Liu Jie,  Zhang Jian-Xun,  Dai Yu. Image enhancement based on multi-guided filtering. Acta Physica Sinica, 2018, 67(23): 238701. doi: 10.7498/aps.67.20181425
    [13] Liu Qi, Wang Li-Dan, Duan Shu-Kai. An adaptive three-Gauss model based on memristive cross array and its application in image enhancement. Acta Physica Sinica, 2017, 66(12): 127301. doi: 10.7498/aps.66.127301
    [14] Wang Cong, Yang Jing, Pan Xiu-Juan, Cai Gao-Hang, Zhao Wei, Zhang Jing-Yuan, Cui Da-Fu, Peng Qin-Jun, Xu Zu-Yan. Image restoration and enhancement based on phase conjugation of optical parametric amplification. Acta Physica Sinica, 2017, 66(10): 104205. doi: 10.7498/aps.66.104205
    [15] Li Hong, Wu Wei, Yang Xiao-Min, Yan Bin-Yu, Liu Kai, Gwanggil Jeon. Multispectral image enhancement based on Retinex by using structure extraction. Acta Physica Sinica, 2016, 65(16): 160701. doi: 10.7498/aps.65.160701
    [16] Bi Guo-Ling, Xu Zhi-Jun, Zhao Jian, Sun Qiang. Multispectral image enhancement based on irradiation-reflection model and bounded operation. Acta Physica Sinica, 2015, 64(10): 100701. doi: 10.7498/aps.64.100701
    [17] Feng Xin, Li Chuan, Hu Kai-Qun. Infrared and visible image fusion based on deep Boltzmann model. Acta Physica Sinica, 2014, 63(18): 184202. doi: 10.7498/aps.63.184202
    [18] Zhao Wen-Da, Zhao Jian, Xu Zhi-Jun. Variational multi-source image fusion based on the structure tensor. Acta Physica Sinica, 2013, 62(21): 214204. doi: 10.7498/aps.62.214204
    [19] Gan Tian, Feng Shao-Tong, Nie Shou-Ping, Zhu Zhu-Qing. Image fusion algorithm based on block-DCT in wavelet domain. Acta Physica Sinica, 2011, 60(11): 114205. doi: 10.7498/aps.60.114205
    [20] Zhang Chuang, Bai Lian-Fa, Zhang Yi. Method of fusing dual-spectrum low light Level images based on gray-scale spatial correlation. Acta Physica Sinica, 2007, 56(6): 3227-3233. doi: 10.7498/aps.56.3227
Metrics
  • Abstract views:  4157
  • PDF Downloads:  121
  • Cited By: 0
Publishing process
  • Received Date:  14 January 2022
  • Accepted Date:  09 February 2022
  • Available Online:  04 March 2022
  • Published Online:  05 June 2022

/

返回文章
返回