Low-light image enhancement algorithm using a residual network with semantic information

doi:10. 19682/ j. cnki. 1005-8885. 2022. 0001

中国邮电高校学报(英文) ›› 2022, Vol. 29 ›› Issue (2): 52-62.doi: 10. 19682/ j. cnki. 1005-8885. 2022. 0001

• Image Processing • 上一篇下一篇

Low-light image enhancement algorithm using a residual network with semantic information

段炼,唐贵进

南京邮电大学

收稿日期:2021-01-19 修回日期:2021-07-10 出版日期:2022-04-26 发布日期:2022-04-26
通讯作者: 唐贵进 E-mail:tanggj@njupt.edu.cn
基金资助:
南京邮电大学科研基金项目;江苏省科协提升计划项目

Low-light image enhancement algorithm using a residual network with semantic information

DUAN Lian¹,

Received:2021-01-19 Revised:2021-07-10 Online:2022-04-26 Published:2022-04-26
Supported by:
This work was supported by the Promotion Plan of Jiangsu Association for Science and Technology (TJ215039), and the Research Foundation of Nanjing University of Posts and Telecommunications (NY219076).

摘要/Abstract

摘要：

Aiming to solve the poor performance of low illumination enhancement algorithms on uneven illumination images, a low-light image enhancement (LIME) algorithm based on a residual network was proposed. The algorithm constructs a deep network that uses residual modules to extract image feature information and semantic modules to extract image semantic information from different levels. Moreover, a composite loss function was also designed for the process of low illumination image enhancement, which dynamically evaluated the loss of an enhanced image from three factors of color, structure, and gradient. It ensures that the model can correctly enhance the image features according to the image semantics, so that the enhancement results are more in line with the human visual experience. Experimental results show that compared with the state-of-the-art algorithms, the semantic-driven residual low-light network (SRLLN) can effectively improve the quality of low illumination images, and achieve better subjective and objective evaluation indexes on different types of images.

关键词: image enhancement, convolutional neural network (CNN), residual learning, image semantic

Abstract:

Key words: image enhancement, convolutional neural network (CNN), residual learning, image semantic

中图分类号:

TP183

DUAN Lian. Low-light image enhancement algorithm using a residual network with semantic information[J]. The Journal of China Universities of Posts and Telecommunications, 2022, 29(2): 52-62.

参考文献

[1] PIZER S M, AMBURN E P, AUSTIN J D, et al. Adaptive histogram equalization and its variations. Computer Vision, Graphics, and Image Processing, 1987, 39(3): 355 -368.

[2] MUNIYAPPAN S, ALLIRANI A, SARASWATHI S. A novel approach for image enhancement by using contrast limited adaptive histogram equalization method. Proceedings of the 4th International Conference on Computing, Communications and Networking Technologies ( ICCCNT’13), 2013, Jul 4 - 6, Tiruchengode, India. Piscataway, NJ, USA: IEEE, 2013: 1 -6.

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

[4] JOBSON D J, RAHMAN Z, WOODELL G A. A multiscale Retinex for bridging the gap between color images and the human observation of scenes. IEEE Transactions on Image processing, 1997, 6(3): 965 -976.

[5] AKHTAR N, MIAN A. Threat of adversarial attacks on deep learning in computer vision: a survey. IEEE Access, 2018, 6: 14410 -14430.

[6] PAK M, KIM S. A review of deep learning in image recognition. Proceedings of the 4th International Conference on Computer Applications and Information Processing Technology (CAIPT’17), 2017, Aug 8 -10, Kuta Bali, Indonesia. Piscataway, NJ, USA: IEEE, 2017: 1 -3.

[7] LI S T, SONG W W, FANG L Y, et al. Deep learning for hyperspectral image classification: an overview. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(9): 6690 -6709.

[8] LI H M. Deep learning for image denoising. International Journal of Signal Processing, Image Processing and Pattern Recognition, 2014, 7(3): 171 -180.

[9] LIM B, SON S, KIM H, et al. Enhanced deep residual networks for single image super-resolution. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition ( CVPR’17 ), 2017, Jul 21 - 26, Honolulu, HI, USA.

Piscataway, NJ, USA: IEEE, 2017: 136 -144.

[10] REN W Q, LIU S F, MA L, et al. Low-light image enhancement via a deep hybrid network. IEEE Transactions on Image Processing, 2019, 28(9): 4364 -4375.

[11] SHEN L, YUE Z H, FENG F, et al. MSR-net: low-light image enhancement using deep convolutional network. arXiv Preprint, arXiv: 1711. 02488, 2017.

[12] TAO L, ZHU C, XIANG G Q, et al. LLCNN: a convolutional neural network for low-light image enhancement. Proceedings of the 2017 IEEE Visual Communications and Image Processing (VCIP’17), 2017, Dec 10 - 13, St Petersburg, FL, USA. Piscataway, NJ, USA: IEEE, 2017: 1 -4.

[13] HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16), 2016,

Jun 27 - 30, Las Vegas, NV, USA. Piscataway, NJ, USA: IEEE, 2016: 770 -778.

[14] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition. arXiv Preprint, arXiv: 1409. 1556, 2014.

[15] IGNATOV A, KOBYSHEV N, TIMOFTE R, et al. DSLR-quality photos on mobile devices with deep convolutional networks. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV’17), 2017, Oct 22 -29, Venice, Italy. Piscataway, NJ, USA: IEEE, 2017: 3277 -3285.

[16] WEI C, WANG W J, YANG W H, et al. Deep Retinex decomposition for low-light enhancement. arXiv Preprint, arXiv:

1808. 04560, 2018.

[17] GUO X J, LI Y, LING H B. LIME: low-light image enhancement via illumination map estimation. IEEE Transactions on Image Processing, 2017, 26(2): 982 -993.

[18] DONG X, WANG G, PANG Y, et al. Fast efficient algorithm for enhancement of low lighting video. Proceedings of the 2011 IEEE International Conference on Multimedia and Expo ( ICME’11), 2011, Jul 11 - 15, Barcelona, Spain. Piscataway, NJ, USA: IEEE, 2011: 1 -6.

[19] LÜF F, LU F, WU J H, et al. MBLLEN: Low-light image/ video enhancement using CNNs. (2018-08-02)[2021-01-18]. http:/ /www. bmva. org/ bmvc/2018/ contents/ papers/0700. pdf.

[20] MITTAL A, SOUNDARARAJAN R, BOVIK A C. Making a“ completely blind’’ image quality analyzer. IEEE Signal

Processing Letters, 2012, 20(3): 209 -212.

Low-light image enhancement algorithm using a residual network with semantic information

Low-light image enhancement algorithm using a residual network with semantic information

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 7

编辑推荐

Metrics

本文评价

[1]	Li Hao, Zhang Linghua, Tong Cheng, Zhou Chenyang. Short-term load forecasting model based on gated recurrent unit and multi-head attention [J]. 中国邮电高校学报(英文版), 2023, 30(3): 25-31.
[2]	Du Rong, Chen Shudong, Li Weiwei, Zhang Xueting, Wang Xianhui, Ge Jin. Data augmentation via joint multi-scale CNN and multi-channel attention for bumblebee image generation [J]. 中国邮电高校学报(英文版), 2023, 30(3): 32-40.
[3]	焦继超陈新平管孟赵亚鑫. TCL: a taxi trajectory prediction model combining time and space features [J]. 中国邮电高校学报(英文版), 2021, 28(3): 63-75.
[4]	季一木, 李可, 刘尚东, 刘强, 尧海昌, 李奎. Collaborative filtering recommendation algorithm based on interactive data classification[J]. 中国邮电高校学报(英文版), 2020, 27(5): 1-12.
[5]	杨健健张强王晓林杜毅博王超吴淼. Research on equipment fault diagnosis method based on random stochastic adaptive particle swarm optimization [J]. 中国邮电高校学报(英文版), 2020, 27(4): 17-25.
[6]	Pang Hao, Bu Yunyun, Wang Cong, Xiao Hui. Automatic detection of breast nodule in the ultrasound images using CNN[J]. 中国邮电高校学报(英文版), 2019, 26(2): 9-16.
[7]	He Jin, Wang Cong, Chen Zhao. Pulmonary tuberculosis detection model of chest X-ray images using convolutional neural network[J]. 中国邮电高校学报(英文版), 2018, 25(6): 1-6.