Volume 21, Issue 2 (10-2024)
JSDP 2024, 21(2): 91-104 |
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Ahmadnia M, Maghrebi M, Ghanbari R. Providing an effective way to enhance low-light images: Enhanced Illumination Map Optimally. JSDP 2024; 21 (2) : 8
URL: http://jsdp.rcisp.ac.ir/article-1-1256-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-1256-en.html
Faculty of Eng,. Ferdowsi Uni. of Mashhad
Abstract: (1049 Views)
Low-light images often suffer from low brightness and contrast, which makes some scene details hard to see. This can affect the performance of many computer vision tasks, such as object recognition, tracking, scene understanding, and occlusion detection. Therefore, it is important and useful to enhance low-light images. One technique to enhance low-light images is based on the Retinex theory, which decomposes images into two components: reflection and illumination. Several mathematical models have been recently developed to estimate the illumination map using this theory. These methods first compute an initial illumination map and then refine it by solving a mathematical model.
This paper introduces a novel method based on the Retinex theory to estimate the illumination map. The proposed method employs a new mathematical model with a differentiable objective function, unlike other similar models. This allows us to use more diverse methods to solve the proposed model, as classical optimization methods such as Newton, Gradient, and Trust-Region methods need the objective function to be differentiable. The proposed model also has linear constraints and is convex, which are desirable properties for optimization. We use the CPLEX solver to solve the proposed model, as it performs well and exploits the features of the model. Finally, we improve the illumination map obtained from the mathematical model using a simple linear transformation.
This paper introduces a new method based on the Retinex theory for enhancing low-light images. The proposed method improves the illumination and the visibility of the scene details. We compare the performance of our method with six existing methods: AMSR, NPE, SRIE, DONG, MF, and LIME. We use four common metrics to evaluate the visual quality of the enhanced images: AMBE, LOE, SSIM, and NIQE. The results demonstrate that our method is competitive with many of the state-of-the-art methods for low-light image enhancement.
This paper introduces a novel method based on the Retinex theory to estimate the illumination map. The proposed method employs a new mathematical model with a differentiable objective function, unlike other similar models. This allows us to use more diverse methods to solve the proposed model, as classical optimization methods such as Newton, Gradient, and Trust-Region methods need the objective function to be differentiable. The proposed model also has linear constraints and is convex, which are desirable properties for optimization. We use the CPLEX solver to solve the proposed model, as it performs well and exploits the features of the model. Finally, we improve the illumination map obtained from the mathematical model using a simple linear transformation.
This paper introduces a new method based on the Retinex theory for enhancing low-light images. The proposed method improves the illumination and the visibility of the scene details. We compare the performance of our method with six existing methods: AMSR, NPE, SRIE, DONG, MF, and LIME. We use four common metrics to evaluate the visual quality of the enhanced images: AMBE, LOE, SSIM, and NIQE. The results demonstrate that our method is competitive with many of the state-of-the-art methods for low-light image enhancement.
Article number: 8
Keywords: Enhance illumination, Enhance low-light images, Illumination map, Retinex theory, Optimization model, Image Processing
Type of Study: Research |
Subject:
Paper
Received: 2021/07/27 | Accepted: 2024/08/22 | Published: 2024/11/4 | ePublished: 2024/11/4
Received: 2021/07/27 | Accepted: 2024/08/22 | Published: 2024/11/4 | ePublished: 2024/11/4
References
1. D. Oneata, J. Revaud, J. Verbeek, and C. Schmid, "Spatio-temporal object detection proposals," 2014, doi: 10.1007/978-3-319-10578-9_48. [DOI:10.1007/978-3-319-10578-9_48]
2. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," 2012, doi: 10.1061/(ASCE)GT.1943-5606.0001284. [DOI:10.1061/(ASCE)GT.1943-5606.0001284]
3. رضائی، معصومه، رضائیان، مهدی و ولی درهمی، «توصیف‌گر موضعی جدید با استفاده از نگاشت مرکاتور به‌منظور تشخیص اشیای سه‌بعدی»، مجلة پردازش علائم و داده‌ها، شمارة 1، صفحات 111-124، 1401
4. K. Zhang, L. Zhang, and M. H. Yang, "Fast Compressive Tracking," IEEE Trans. Pattern Anal. Mach. Intell., 2014, doi: 10.1109/TPAMI.2014.2315808. [DOI:10.1109/TPAMI.2014.2315808]
5. Y. Liu, R. R. Martin, L. De Dominicis, and B. Li, "Using retinex for point selection in 3D shape registration," Pattern Recognit., 2014, doi: 10.1016/j.patcog.2013.12.015. [DOI:10.1016/j.patcog.2013.12.015]
6. C. Jung, T. Sun, and L. Jiao, "Eye detection under varying illumination using the retinex theory," Neurocomputing, 2013, doi: 10.1016/j.neucom.2013.01.038. [DOI:10.1016/j.neucom.2013.01.038]
7. C. Couprie, C. Farabet, L. Najman, and Y. LeCun, "Indoor semantic segmentation using depth information," 2013.
8. J. K. W. Wong, M. Maghrebi, A. Ahmadian Fard Fini, M. A. Alizadeh Golestani, M. Ahmadnia, and M. Er, "Development of a refined illumination and reflectance approach for optimal construction site interior image enhancement," Constr. Innov., 2024, doi: 10.1108/CI-02-2022-0044. [DOI:10.1108/CI-02-2022-0044]
9. احمدی، سید محمد و دیانت، روح الله، «یک چارچوب توزیعی مبتنی‌بر خوشه‌بندی دومرحله‌ای برای شناسایی چهره درمقیاس بالا»، مجله پردازش علائم و داده‌ها، شمارة 1، صفحات 53-70، 1403
10. S. Sarkar, V. Venugopalan, K. Reddy, M. Giering, J. Ryde, and N. Jaitly, "Occlusion Edge Detection in RGB-D Frames using Deep Convolutional Networks," 2014, [Online]. Available: http://arxiv.org/abs/1412.7007.
11. K. G. Lore, A. Akintayo, and S. Sarkar, "LLNet: A deep autoencoder approach to natural low-light image enhancement," Pattern Recognit., 2017, doi: 10.1016/j.patcog.2016.06.008. [DOI:10.1016/j.patcog.2016.06.008]
12. W. Kim, R. Lee, M. Park, and S. H. Lee, "Low-Light Image Enhancement Based on Maximal Diffusion Values," IEEE Access, 2019, doi: 10.1109/ACCESS.2019.2940452. [DOI:10.1109/ACCESS.2019.2940452]
13. X. Guo, Y. Li, and H. Ling, "LIME: Low-light image enhancement via illumination map estimation," IEEE Trans. Image Process., 2017, doi: 10.1109/TIP.2016.2639450. [DOI:10.1109/TIP.2016.2639450]
14. S. Der Chen and A. R. Ramli, "Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation," IEEE Trans. Consum. Electron., 2003, doi: 10.1109/TCE.2003.1261233. [DOI:10.1109/TCE.2003.1261233]
15. T. Arici, S. Dikbas, and A. Altunbasak, "A histogram modification framework and its application for image contrast enhancement," IEEE Trans. Image Process., 2009, doi: 10.1109/TIP.2009.2021548. [DOI:10.1109/TIP.2009.2021548]
16. M. Veluchamy and B. Subramani, "Image contrast and color enhancement using adaptive gamma correction and histogram equalization," Optik (Stuttg)., 2019, doi: 10.1016/j.ijleo.2019.02.054. [DOI:10.1016/j.ijleo.2019.02.054]
17. [K. Srinivas and A. K. Bhandari, "Low light image enhancement with adaptive sigmoid transfer function," IET Image Process., vol. 14, no. 4, pp. 668-678, 2020, doi: 10.1049/iet-ipr.2019.0781. [DOI:10.1049/iet-ipr.2019.0781]
18. M. A. Al Wadud, M. H. Kabir, M. A. A. Dewan, and O. Chae, "A dynamic histogram equalization for image contrast enhancement," IEEE Trans. Consum. Electron., 2007, doi: 10.1109/TCE.2007.381734. [DOI:10.1109/TCE.2007.381734]
19. Edwin H. Land, "The Retinex Theory of Color Vision," Sci. Am., 1977. [DOI:10.1038/scientificamerican1277-108]
20. X. Fu, D. Zeng, Y. Huang, X. P. Zhang, and X. Ding, "A Weighted Variational Model for Simultaneous Reflectance and Illumination Estimation," 2016, doi: 10.1109/CVPR.2016.304. [DOI:10.1109/CVPR.2016.304]
21. D. Parihar, A. Singh, and K. Singh, "Illumination Estimation for Nature Preserving low-light image enhancement," pp. 1-11, 2020. [DOI:10.36227/techrxiv.12236780]
22. M. K. Ng and W. Wang, "A Total Variation Model for Retinex," SIAM J. Imaging Sci., 2011, doi: 10.1137/100806588. [DOI:10.1137/100806588]
23. S. Wang, J. Zheng, H. M. Hu, and B. Li, "Naturalness preserved enhancement algorithm for non-uniform illumination images," IEEE Trans. Image Process., 2013, doi: 10.1109/TIP.2013.2261309. [DOI:10.1109/TIP.2013.2261309]
24. G. Fu, L. Duan, and C. Xiao, "A Hybrid L2 -LP Variational Model for Single Low-Light Image Enhancement with Bright Channel Prior," 2019, doi: 10.1109/ICIP.2019.8803197. [DOI:10.1109/ICIP.2019.8803197]
25. D. J. Jobson, Z. U. 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., 1997, doi: 10.1109/83.597272. [DOI:10.1109/83.597272]
26. D. J. Jobson, Z. U. Rahman, and G. A. Woodell, "Properties and performance of a center/surround retinex," IEEE Trans. Image Process., 1997, doi: 10.1109/83.557356. [DOI:10.1109/83.557356]
27. W. Wang, X. Wu, X. Yuan, and Z. Gao, "An Experiment-Based Review of Low-Light Image Enhancement Methods," IEEE Access. 2020, doi: 10.1109/ACCESS.2020.2992749. [DOI:10.1109/ACCESS.2020.2992749]
28. Y. Chang, C. Jung, P. Ke, H. Song, and J. Hwang, "Automatic Contrast-Limited Adaptive Histogram Equalization with Dual Gamma Correction," IEEE Access, 2018, doi: 10.1109/ACCESS.2018.2797872. [DOI:10.1109/ACCESS.2018.2797872]
29. R. Kimmel, M. Elad, D. Shaked, R. Keshet, and I. Sobel, "A variational framework for retinex," Int. J. Comput. Vis., 2003, doi: 10.1023/A:1022314423998. [DOI:10.1023/A:1022314423998]
30. J. Y. Kim, L. S. Kim, and S. H. Hwang, "An advanced contrast enhancement using partially overlapped sub-block histogram equalization," IEEE Trans. Circuits Syst. Video Technol., 2001, doi: 10.1109/76.915354. [DOI:10.1109/76.915354]
31. Y. S. Chiu, F. C. Cheng, and S. C. Huang, "Efficient contrast enhancement using adaptive gamma correction and cumulative intensity distribution," 2011, doi: 10.1109/ICSMC.2011.6084119. [DOI:10.1109/ICSMC.2011.6084119]
32. S. C. Huang, F. C. Cheng, and Y. S. Chiu, "Efficient contrast enhancement using adaptive gamma correction with weighting distribution," IEEE Trans. Image Process., 2013, doi: 10.1109/TIP.2012.2226047. [DOI:10.1109/TIP.2012.2226047]
33. C. R. Nithyananda, A. C. Ramachandra, and Preethi, "Review on Histogram Equalization based Image Enhancement Techniques," 2016, doi: 10.1109/ICEEOT.2016.7755145. [DOI:10.1109/ICEEOT.2016.7755145]
34. X. Fu, D. Zeng, Y. Huang, Y. Liao, X. Ding, and J. Paisley, "A fusion-based enhancing method for weakly illuminated images," Signal Processing, 2016, doi: 10.1016/j.sigpro.2016.05.031. [DOI:10.1016/j.sigpro.2016.05.031]
35. Y. T. Kim, "Contrast enhancement using brightness preserving bi-histogram equalization," IEEE Trans. Consum. Electron., 1997, doi: 10.1109/30.580378. [DOI:10.1109/30.580378]
36. Q. Wang and R. K. Ward, "Fast image/video contrast enhancement based on weighted thresholded histogram equalization," IEEE Trans. Consum. Electron., 2007, doi: 10.1109/TCE.2007.381756. [DOI:10.1109/TCE.2007.381756]
37. S. Der Chen and A. R. Ramli, "Minimum mean brightness error bi-histogram equalization in contrast enhancement," IEEE Trans. Consum. Electron., 2003, doi: 10.1109/TCE.2003.1261234. [DOI:10.1109/TCE.2003.1261234]
38. N. Kong, "A Literature Review on Histogram Equalization and Its Variations for Digital Image Enhancement," Int. J. Innov. Manag. Technol., 2013, doi: 10.7763/ijimt.2013.v4.426. [DOI:10.7763/IJIMT.2013.V4.426]
39. T. K. Kim, J. K. Paik, and B. S. Kang, "Contrast enhancement system using spatially adaptive histogram equalization with temporal filtering," IEEE Trans. Consum. Electron., 1998, doi: 10.1109/30.663733. [DOI:10.1109/30.663733]
40. A. M. Reza, "Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement," J. VLSI Signal Process. Syst. Signal Image. Video Technol., 2004, doi: 10.1023/B:VLSI.0000028532.53893.82. [DOI:10.1023/B:VLSI.0000028532.53893.82]
41. B. Liu, W. Jin, Y. Chen, C. Liu, and L. Li, "Contrast enhancement using non-overlapped sub-blocks and local histogram projection," IEEE Trans. Consum. Electron., 2011, doi: 10.1109/TCE.2011.5955195. [DOI:10.1109/TCE.2011.5955195]
42. [R. R. Hussein, Y. I. Hamodi, and R. A. Sabri, "Retinex theory for color image enhancement: A systematic review," International Journal of Electrical and Computer Engineering. 2019, doi: 10.11591/ijece.v9i6.pp5560-5569. [DOI:10.11591/ijece.v9i6.pp5560-5569]
43. H. Chang, M. K. Ng, W. Wang, and T. Zeng, "Retinex image enhancement via a learned dictionary," Opt. Eng., 2015, doi: 10.1117/1.oe.54.1.013107. [DOI:10.1117/1.OE.54.1.013107]
44. Y. O. Nam, D. Y. Choi, and B. C. Song, "Power-constrained contrast enhancement algorithm using multiscale retinex for OLED display," IEEE Trans. Image Process., 2014, doi: 10.1109/TIP.2014.2324288. [DOI:10.1109/TIP.2014.2324288]
45. B. Gu, W. Li, M. Zhu, and M. Wang, "Local edge-preserving multiscale decomposition for high dynamic range image tone mapping," IEEE Trans. Image Process., 2013, doi: 10.1109/TIP.2012.2214047. [DOI:10.1109/TIP.2012.2214047]
46. S. Pan, X. An, and H. He, "Adapting iterative retinex computation for high-dynamic-range tone mapping," J. Electron. Imaging, 2013, doi: 10.1117/1.jei.22.2.023006. [DOI:10.1117/1.JEI.22.2.023006]
47. X. Lan, H. Shen, L. Zhang, and Q. Yuan, "A spatially adaptive retinex variational model for the uneven intensity correction of remote sensing images," Signal Processing, 2014, doi: 10.1016/j.sigpro.2014.01.017. [DOI:10.1016/j.sigpro.2014.01.017]
48. G. A. Rahman, Z. U., Jobson, D. J., & Woodell, "Retinex processing for automatic image enhancement," J. Electron. Imaging, 2002. [DOI:10.1117/12.469537]
49. M. Li, J. Liu, W. Yang, X. Sun, and Z. Guo, "Structure-Revealing Low-Light Image Enhancement Via Robust Retinex Model," IEEE Trans. Image Process., 2018, doi: 10.1109/TIP.2018.2810539. [DOI:10.1109/TIP.2018.2810539]
50. X. Ren, M. Li, W. H. Cheng, and J. Liu, "Joint Enhancement and Denoising Method via Sequential Decomposition," 2018, doi: 10.1109/ISCAS.2018.8351427. [DOI:10.1109/ISCAS.2018.8351427]
51. Z. Huang, T. Zhang, Q. Li, and H. Fang, "Adaptive gamma correction based on cumulative histogram for enhancing near-infrared images," Infrared Phys. Technol., 2016, doi: 10.1016/j.infrared.2016.11.001. [DOI:10.1016/j.infrared.2016.11.001]
52. K. León, D. Mery, F. Pedreschi, and J. León, "Color measurement in L*a*b* units from RGB digital images," Food Res. Int., 2006, doi: 10.1016/j.foodres.2006.03.006. [DOI:10.1016/j.foodres.2006.03.006]
53. T. Kumar and K. Verma, "A Theory Based on Conversion of RGB image to Gray image," Int. J. Comput. Appl., 2010, doi: 10.5120/1140-1493. [DOI:10.5120/1140-1493]
54. S. Nocedal, Jorge and Wright, "Numerical optimization," Springer Sci. & Bus. Media, 2006.
55. R. Anand, D. Aggarwal, and V. Kumar, "A comparative analysis of optimization solvers," J. Stat. Manag. Syst., 2017, doi: 10.1080/09720510.2017.1395182. [DOI:10.1080/09720510.2017.1395182]
56. Z. Wang, A. C. Bovik, and L. Lu, "Why is image quality assessment so difficult?," ICASSP, IEEE Int. Conf. Acoust. Speech Signal Process. - Proc., 2002, doi: 10.1109/ICASSP.2002.5745362. [DOI:10.1109/ICASSP.2002.5745362]
57. Z. Ying, G. Li, and W. Gao, "A bio-inspired multi-exposure fusion framework for low-light image enhancement," arXiv. 2017.
58. 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., 2004, doi: 10.1109/TIP.2003.819861. [DOI:10.1109/TIP.2003.819861]
59. A. Mittal, R. Soundararajan, and A. C. Bovik, "Making a 'completely blind' image quality analyzer," IEEE Signal Process. Lett., 2013, doi: 10.1109/LSP.2012.2227726. [DOI:10.1109/LSP.2012.2227726]
60. عابدي، زهرا و یزدیان دهکردي، مهدي، «توسعة روش‌های مبتنی بر رفع نوفه اسپکل تصویر جهت رفع نوفه ویدئو ویسار»، مجله پردازش علائم و داده‌ها، شمارة 1، صفحات 137-152، 1401
61. C. H. Lee, J. L. Shih, C. C. Lien, and C. C. Han, "Adaptive multiscale retinex for image contrast enhancement," 2013, doi: 10.1109/ SITIS.2013.19. [DOI:10.1109/SITIS.2013.19]
62. X. Dong et al., "Fast efficient algorithm for enhancement of low lighting video," 2011, doi: 10.1109/ICME.2011.6012107. [DOI:10.1109/ICME.2011.6012107]
63. C. Lee, C. S. Kim, and C. Lee, "Contrast enhancement based on layered difference representation of 2D histograms," IEEE Trans. Image Process., 2013, doi: 10.1109/TIP. 2013.2284059. [DOI:10.1109/TIP.2013.2284059]
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