Enhancement of Unevenly Illuminated Images: An Experiment-Based Review

Zainab Khalid Younis

Abstract


Low-lighting conditions pose significant challenges to captured images and result in degraded image quality, characterized by poor visibility, imbalanced illumination, increased noise, limited contrast, inaccurate colours, and loss of detail. In recent years, the development of effective low-light enhancement techniques has attracted considerable attention from researchers and practitioners in various fields, such as surveillance, photography, forensics, and medical imaging. This article comprehensively overviews advances in low-light image enhancement methods, techniques, and algorithms. This review summarizes the working mechanism for each reviewed algorithm, implements it, provides the results, and analyses them, highlighting the concept, advantages, and disadvantages. Overall, this review offers a comprehensive resource for researchers and practitioners interested in knowing the latest technologies and methods for low-light image enhancement. It provides insights into current challenges, promising solutions, and future directions for advancing the field of low-light imaging. Finally, it benefits various researchers by describing the available concepts, what pros to consider, and what cons to avoid when developing their algorithms.


Keywords


Low-light, Image enhancement, Uneven illumination, Image processing.

Full Text:

PDF

References


M. R. Banham and A. K. Katsaggelos, “Digital image restoration,” IEEE Signal Process. Mag., vol. 14, no. 2, pp. 24–41, 1997.

Y. Qi et al., “A comprehensive overview of image enhancement techniques,” Arch. Comput. Methods Eng., vol. 29, no. 1, pp. 583–607, 2022

J. Li, S. Hao, T. Li, L. Zhuo, and J. Zhang, “RDMA: low-light image enhancement based on retinex decomposition and multiscale adjustment,” International Journal of Machine Learning and Cybernetics, pp. 1–17, 2023

P. Karuppusamy, “Techniques for enhancement and denoising of underwater images: A Review,” December 2019, vol. 1, no. 02, pp. 81–90, 2019.

N. Dey, “Uneven illumination correction of digital images: A survey of the state-of-the-art,” Optik (Stuttg.), vol. 183, pp. 483–495, 2019.

Y. P. Loh and C. S. Chan, “Getting to know low-light images with the Exclusively Dark dataset,” Comput. Vis. Image Underst., vol. 178, pp. 30–42, 2019.

S. D. Thepade and P. M. Pardhi, “Contrast enhancement with brightness preservation of low light images using a blending of CLAHE and BPDHE histogram equalization methods,” Int. J. Inf. Technol., vol. 14, no. 6, pp. 3047–3056, 2022.

Y. Zhang, X. Guo, J. Ma, W. Liu, and J. Zhang, “Beyond brightening low-light images,” Int. J. Comput. Vis., vol. 129, no. 4, pp. 1013–1037, 2021.

K. M. Gerhardsson, T. Laike, and M. Johansson, “Leaving lights on-a conscious choice or wasted light? Use of indoor lighting in Swedish homes. Indoor and Built Environment,” vol. 30, pp. 745–762, 2021.

K. Li, Y. Chen, and Y. Li, “The Random Forest-based method of fine-resolution population spatialization by using the International Space Station nighttime photography and social sensing data,” Remote Sens. (Basel), vol. 10, no. 10, p. 1650, 2018.

M. Jian, X. Liu, H. Luo, X. Lu, H. Yu, and J. Dong, “Underwater image processing and analysis: A review,” Signal Process. Image Commun., vol. 91, no. 116088, p. 116088, 2021.

Z. Zheng, Y. Wu, X. Han, and J. Shi, “ForkGAN: Seeing into the rainy night,” in Computer Vision – ECCV 2020, Cham: Springer International Publishing, 2020, pp. 155–170.

M. Purohit, A. Chakraborty, A. Kumar, and B. K. Kaushik, “Image processing framework for performance enhancement of low-light image sensors,” IEEE Sens. J., vol. 21, no. 6, pp. 8530–8542, 2021.

M. Purohit, A. Chakraborty, A. Kumar, and B. K. Kaushik, “Image processing framework for performance enhancement of low-light image sensors,” IEEE Sens. J., vol. 21, no. 6, pp. 8530–8542, 2021.

M. Veluchamy and B. Subramani, “Image contrast and color enhancement using adaptive gamma correction and histogram equalization,” Optik (Stuttg.), vol. 183, pp. 329–337, 2019.

W. Wang, X. Wu, X. Yuan, and Z. Gao, “An experiment-based review of low-light image enhancement methods,” IEEE Access, vol. 8, pp. 87884–87917, 2020.

C. Peters and S. Allan, “Everyday imagery: Users’ reflections on smartphone cameras and communication,” Converg. Int. J. Res. New Media Technol., vol. 24, no. 4, pp. 357–373, 2018.

V. Tsakanikas and T. Dagiuklas, “Video surveillance systems-current status and future trends,” Comput. Electr. Eng., 2017.

G. Overgoor, M. Chica, W. Rand, and A. Weishampel, “Letting the computers take over: Using AI to solve marketing problems,” Calif. Manage. Rev., vol. 61, no. 4, pp. 156–185, 2019.

C. Li et al., “Low-light image and video enhancement using deep learning: A survey,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 12, pp. 9396–9416, 2022.

M. Mukaida, S. Kojima, E. Uchino, and N. Suetake, “Low-light image enhancement method by soft-closing using local histogram,” in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE), 2021.

K. Lu and L. Zhang, “TBEFN: A two-branch exposure-fusion network for low-light image enhancement,” IEEE Trans. Multimedia, vol. 23, pp. 4093–4105, 2021.

X. Ren, W. Yang, W.-H. Cheng, and J. Liu, “LR3M: Robust low-light enhancement via low-Rank Regularized Retinex Model,” IEEE Trans. Image Process., vol. 29, pp. 5862–5876, 2020.

A. Sobbahi and R. Tekli, “Comparing deep learning models for low-light natural scene image enhancement and their impact on object detection and classification: Overview, empirical evaluation, and challenges,” in Signal Processing: Image Communication, 2022.

Y. Liu, P. Jia, H. Zhou, and A. Wang, “Joint dehazing and denoising for single nighttime image via multiscale decomposition,” Multimedia Tools and Applications, vol. 81, pp. 23941–23962, 2022.

F. I. Eyiokur, D. Yaman, H. K. Ekenel, and A. Waibel, “Exposure correction model to enhance image quality,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2022.

J. R. Jebadass and P. Balasubramaniam, “Low light enhancement algorithm for color images using intuitionistic fuzzy sets with histogram equalization,” Multimed. Tools Appl., 2022.

S. Malik and R. Soundararajan, “A low light natural image statistical model for joint contrast enhancement and denoising,” Signal Process. Image Commun., vol. 99, no. 116433, p. 116433, 2021.

A. Sharma and R. T. Tan, “Nighttime visibility enhancement by increasing the dynamic range and suppression of light effects,” in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021.

S. U. Rehman et al., “DRA-Net: densely residual attention based low-light image enhancement,” Fourteenth International Conference on Graphics and Image Processing, vol. 12705, pp. 674–685, 2023.

X. Fu, D. Zeng, Y. Huang, X. Ding, and X.-P. Zhang, “A variational framework for single low light image enhancement using bright channel prior,” in 2013 IEEE Global Conference on Signal and Information Processing, 2013.

X. Fu, Y. Sun, M. LiWang, Y. Huang, X.-P. Zhang, and X. Ding, “A novel retinex based approach for image enhancement with illumination adjustment,” in 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014.

X. Fu, Y. Liao, D. Zeng, Y. Huang, X.-P. Zhang, and X. Ding, “A probabilistic method for image enhancement with simultaneous illumination and reflectance estimation,” IEEE Trans. Image Process., vol. 24, no. 12, pp. 4965–4977, 2015.

X. Fu, D. Zeng, Y. Huang, X.-P. Zhang, and X. Ding, “A weighted variational model for simultaneous reflectance and illumination estimation,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

S.Y. Yu and H. Zhu, “Low-illumination image enhancement algorithm based on a physical lighting model,” IEEE Trans. Circuits Syst. Video Technol., vol. 29, no. 1, pp. 28–37, 2019.

Y. Ren, Z. Ying, T. H. Li, and G. Li, “LECARM: Low-light image enhancement using the camera response model,” IEEE Trans. Circuits Syst. Video Technol., vol. 29, no. 4, pp. 968–981, 2019.

C. Li, J. Guo, F. Porikli, and Y. Pang, “LightenNet: A Convolutional Neural Network for weakly illuminated image enhancement,” Pattern Recognit. Lett., vol. 104, pp. 15–22, 2018.

W. Wang, Z. Chen, X. Yuan, and X. Wu, “Adaptive image enhancement method for correcting low-illumination images,” Inf. Sci. (Ny), vol. 496, pp. 25–41, 2019.

M. Tanaka, T. Shibata, and M. Okutomi, “Gradient-based low-light image enhancement,” in 2019 IEEE International Conference on Consumer Electronics (ICCE), 2019.

G. Fu, L. Duan, and C. Xiao, “A hybrid L 2− L p variational model for single low-light image enhancement with bright channel prior,” in 2019 IEEE International conference on image processing (ICIP), IEEE, 2019, pp. 1925–1929.

M. A. Al-Hashim and Z. Al-Ameen, “Retinex-based multiphase algorithm for low-light image enhancement,” Trait. Du Signal, vol. 37, no. 5, pp. 733–743, 2020.

W. Yu, H. Yao, D. Li, G. Li, and H. Shi, “GLAGC: Adaptive dual-gamma function for image illumination perception and correction in the wavelet domain,” Sensors (Basel), vol. 21, no. 3, p. 845, 2021.

M. F. Hassan, T. Adam, H. Rajagopal, and R. Paramesran, “A hue preserving uniform illumination image enhancement via triangle similarity criterion in HSI color space,” Vis. Comput., vol. 39, no. 12, pp. 6755–6766, 2023.

S. Wang, J. Zheng, H.-M. 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.

Y. Fang, K. Ma, Z. Wang, W. Lin, Z. Fang, and G. Zhai, “No-Reference Quality Assessment for Contrast-Distorted Images Based on Natural Scene Statistics”,” IEEE Signal Processing Letters, vol. 22, no. 7, pp. 838–842, 2014.

K. Gu et al., “Blind quality assessment of tone mapped images via analysis of information, naturalness, and structure,” IEEE Transactions on Multimedia, vol. 18, no. 3, 2016.

Y. Peng, S. Tu, and J. Qian, “Low-light image enhancement network based on luminance prior and depth feature extraction,” in 2024 5th International Conference on Computer Vision, Image and Deep Learning (CVIDL), 2024.

L. Shen, M. Reda, X. Zhang, Y. Zhao, and S. G. Kong, “Polarization-driven solution for mitigating scattering and uneven illumination in underwater imagery,” IEEE Trans. Geosci. Remote Sens., vol. 62, pp. 1–15, 2024.

K. Singh and A. S. Parihar, “Illumination estimation for nature preserving low-light image enhancement,” Vis. Comput., 2023.

M. Kumar, A. K. Bhandari, and M. Jha, “Unevenly illuminated image distortion correction using brightness perception and chromatic luminance,” Multimedia Tools and Applications, vol. 83, no. 6, pp. 17395–17428, 2024.




DOI: https://doi.org/10.33387/protk.v12i2.8533

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.



Editorial Office :
Protek : Jurnal Ilmiah Teknik Elektro
Department of Electrical Engineering. Faculty of Engineering. Universitas Khairun.
Address: Jusuf Abdulrahman 53 Gambesi, Ternate City, Indonesia.
Email: protek@unkhair.ac.id, WhatsApp: +6282292852552
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

View Stat Protek