AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Tsinghua Science and Technology Article
PDF (15.1 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

DEANet: Decomposition Enhancement and Adjustment Network for Low-Light Image Enhancement

College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
Show Author Information

Abstract

Poor illumination greatly affects the quality of obtained images. In this paper, a novel convolutional neural network named DEANet is proposed on the basis of Retinex for low-light image enhancement. DEANet combines the frequency and content information of images and is divided into three subnetworks: decomposition, enhancement, and adjustment networks, which perform image decomposition; denoising, contrast enhancement, and detail preservation; and image adjustment and generation, respectively. The model is trained on the public LOL dataset, and the experimental results show that it outperforms the existing state-of-the-art methods regarding visual effects and image quality.

Keywords

low-light image enhancementRetineximage decompositionimage adjustment

Electronic Supplementary Material

Download File(s)
743-753ESM.pdf (17.3 MB)

References

[1]
E. H. Land, The Retinex theory of color vision, Sci. Am., vol. 237, no. 6, pp. 108–128, 1977.
Crossref Google Scholar
[2]
D. J. Jobson, Z. Rahman, and G. A. Woodell, Properties and performance of a center/surround Retinex, IEEE Trans. Image Process., vol. 6, no. 3, pp. 451–462, 1997.
Crossref Google Scholar
[3]
D. J. Jobson, Z. Rahman, and G. A. Woodell, A multiscale Retinex for bridging the gap between color images and the human observation of scenes, IEEE Trans. Image Process., vol. 6, no. 7, pp. 965–976, 1997.
Crossref Google Scholar
[4]
Z. Ying, G. Li, and W. Gao, A bio-inspired multi-exposure fusion framework for low-light image enhancement, arXiv preprint arXiv: 1711.00591, 2017.
Google Scholar
[5]
X. Guo, Y. Li, and H. Ling, LIME: Low-light image enhancement via illumination map estimation, IEEE Trans. Image Process., vol. 26, no. 2, pp. 982–993, 2017.
Crossref Google Scholar
[6]
K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, Image denoising by sparse 3-D transform-domain collaborative filtering, IEEE Trans. Image Process., vol. 16, no. 8, pp. 2080–2095, 2007.
Crossref Google Scholar
[7]
J. Zhong, B. Yang, G. Huang, F. Zhong, and Z. Chen, Remote sensing image fusion with convolutional neural network, Sens. Imag., vol. 17, no. 1, p. 10, 2016.
Crossref Google Scholar
[8]
Z. Zhu, Y. Luo, H. Wei, Y. Li, G. Qi, N. Mazur, Y. Li, and P. Li, Atmospheric light estimation based remote sensing image dehazing, Remote Sens., vol. 13, no. 13, p. 2432, 2021.
Crossref Google Scholar
[9]
Z. Zhu, H. Wei, G. Hu, Y. Li, G. Qi, and N. Mazur, A novel fast single image dehazing algorithm based on artificial multiexposure image fusion, IEEE Trans. Instrum. Meas., vol. 70, p. 5001523, 2021.
Crossref Google Scholar
[10]
K. G. Lore, A. Akintayo, and S. Sarkar, LLNet: A deep autoencoder approach to natural low-light image enhancement, Pattern Recognit., vol. 61, pp. 650–662, 2017.
Crossref Google Scholar
[11]
C. Wei, W. Wang, W. Yang, and J. Liu, Deep Retinex decomposition for low-light enhancement, presented at the British Machine Vision Conf., Newcastle, UK, 2018.
Google Scholar
[12]
F. Lv, F. Lu, J. Wu, and C. Lim, MBLLEN: Low-light image/video enhancement using CNNs, presented at the British Machine Vision Conf., Newcastle, UK, 2018.
Google Scholar
[13]
S. Lim and W. Kim, DSLR: Deep stacked Laplacian restorer for low-light image enhancement, IEEE Trans. Multimedia, vol. 23, pp. 4272–4284, 2021.
Crossref Google Scholar
[14]
W. Wang, C. Wei, W. Yang, and J. Liu, GLADNet: Low-light enhancement network with global awareness, in Proc. 13th IEEE Int. Conf. on Automatic Face & Gesture Recognition, Xi’an, China, 2018, pp. 751–755.
Crossref Google Scholar
[15]
Y. Zhang, X. Guo, J. Ma, W. Liu, and J. Zhang, Beyond brightening low-light images, Int. J. Comput. Vision, vol. 129, no. 4, pp. 1013–1037, 2021.
Crossref Google Scholar
[16]
Z. Zhao, B. Xiong, L. Wang, Q. Ou, L. Yu, and F. Kuang, RetinexDIP: A unified deep framework for low-light image enhancement, IEEE Trans. Circuits Syst. Video Technol., vol. 32, no. 3, pp. 1076–1088, 2022.
Crossref Google Scholar
[17]
W. Wang, Z. Chen, and X. Yuan, Simple low-light image enhancement based on Weber-Fechner law in logarithmic space, Signal Process.: Image Commun., vol. 106, p. 116742, 2022.
Crossref Google Scholar
[18]
D. Liang, L. Li, M. Wei, S. Yang, L. Zhang, W. Yang, Y. Du, and H. Zhou, Semantically contrastive learning for low-light image enhancement, in Proc. 36th AAAI Conf. on Artificial Intelligence, 2022, pp. 1555–1563.
Crossref Google Scholar
[19]
Z. Ying, G. Li, Y. Ren, R. Wang, and W. Wang, A new low-light image enhancement algorithm using camera response model, in Proc. 2017 IEEE Int. Conf. on Computer Vision Workshops, Venice, Italy, 2017, pp. 3015–3022.
Crossref Google Scholar
[20]
A. Zhu, L. Zhang, Y. Shen, Y. Ma, S. Zhao, and Y. Zhou, Zero-Shot restoration of underexposed Images via robust Retinex decomposition, in Proc. 2020 IEEE Int. Conf. on Multimedia and Expo, London, UK, 2020, pp. 1–6.
Crossref Google Scholar
[21]
C. Guo, C. Y. Li, J. Guo, C. C. Loy, J. Hou, S. Kwong, and R. Cong, Zero-reference deep curve estimation for low-light image enhancement, in Proc. 2020 IEEE/CVF Conf. on Computer Vision and Pattern Recognition, Seattle, WA, USA, 2020, pp. 1777–1786.
Crossref Google Scholar
[22]
Y. Jiang, X. Gong, D. Liu, Y. Cheng, C. Fang, X. Shen, J. Yang, P. Zhou, and Z. Wang, EnlightenGAN: Deep light enhancement without paired supervision, IEEE Trans. Image Process., vol. 30, pp. 2340–2349, 2021.
Crossref Google Scholar
[23]
S. Wang, J. Zheng, H. Hu, and B. Li, Naturalness preserved enhancement algorithm for non-uniform illumination images, IEEE Trans. Image Process., vol. 22, no. 9, pp. 3538–3548, 2013.
Crossref Google Scholar
[24]
C. Lee, C. Lee, Y. Y. Lee, and C. Kim, Power-constrained contrast enhancement for emissive displays based on histogram equalization, IEEE Trans. Image Process., vol. 21, no. 1, pp. 80–93, 2012.
Crossref Google Scholar
[25]
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, Image quality assessment: From error visibility to structural similarity, IEEE Trans. Image Process., vol. 13, no. 4, pp. 600–612, 2004.
Crossref Google Scholar
[26]
L. Zhang, L. Zhang, X. Mou, and D. Zhang, FSIM: A feature similarity index for image quality assessment, IEEE Trans. Image Process., vol. 20, no. 8, pp. 2378–2386, 2011.
Crossref Google Scholar
[27]
W. Xue, L. Zhang, X. Mou, and A. C. Bovik, Gradient magnitude similarity deviation: A highly efficient perceptual image quality index, IEEE Trans. Image Process., vol. 23, no. 2, pp. 684–695, 2014.
Crossref Google Scholar
[28]
A. Mittal, R. Soundararajan, and A. C. Bovik, Making a “completely blind” image quality analyzer, IEEE Signal Process. Lett., vol. 20, no. 3, pp. 209–212, 2013.
Crossref Google Scholar
Tsinghua Science and Technology
Volume 28 Issue 4,
August 2023
Pages 743-753
DOI: 10.26599/TST.2022.9010047
Cite this article:
Jiang Y, Li L, Zhu J, et al. DEANet: Decomposition Enhancement and Adjustment Network for Low-Light Image Enhancement. Tsinghua Science and Technology, 2023, 28(4): 743-753. https://doi.org/10.26599/TST.2022.9010047

509

Views

67

Downloads

7

Crossref

5

Web of Science

6

Scopus

0

CSCD

Altmetrics
Received: 21 May 2022
Revised: 10 August 2022
Accepted: 08 October 2022
Published: 06 January 2023
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Return