NEWS

ILLUMINATION ENHANCEMENT OF NIGHTTIME IMAGES USING A REGULATED SINGLE SCALE RETINEX ALGORITHM


(Received: 14-Jan.-2024, Revised: 11-Mar.-2024 , Accepted: 13-Mar.-2024)
Nowadays, people are active during the nighttime and take many photos to record their activities. Due to the low- light nature of the environment at nighttime, captured images tend to appear with dimmed and imbalanced illumination, limited contrast, covert noise and diminished colors. Thus, this paper presents a practical algorithm to improve the illumination of nighttime images based on the single-scale retinex model, image processing methods and certain statistical functions. The developed algorithm initiates by converting the image from the RGB into the HSV model. Then, it enhances only the value (V) channel while preserving the H and S channels. Next, estimating the illumination version of the image and calculating the logarithms of both the illumination and original image are performed. Afterward, a logarithmic subtraction occurs and a modified cumulative distribution function of Gumble probability is applied and the result is further enhanced using a logarithmic transform method. These operations produce the processed V channel and a conversion to the RGB format occurs to generate the final output. The proposed algorithm is experimented with by using two datasets, compared to ten different contemporary algorithms and outcomes are evaluated via three sophisticated metrics. Based on the attained results, promising performances by the developed algorithm have been recorded, surpassing the performance of many existing algorithms in various objective, subjective and runtime terms.

[1] Y. F. Wang, H. M. Liu and Z. W. Fu, "Low-light Image Enhancement via the Absorption Light Scattering Model," IEEE Transactions on Image Processing, vol. 28, no. 11, pp. 5679–5690, 2019.

[2] X. Guo, Y. Li and H. Ling, "LIME: Low-light Image Enhancement via Illumination Map Estimation," IEEE Transactions on Image Processing, vol. 26, no. 2, pp. 982–993, 2017.

[3] Y. Qi et al., "A Comprehensive Overview of Image Enhancement Techniques," Archives of Computational Methods in Engineering, vol. 29, no. 1, pp. 583–607, 2022.

[4] M. Li, J. Liu, W. Yang, X. Sun and Z. Guo, "Structure-revealing Low-light Image Enhancement via Robust Retinex Model," IEEE Transactions on Image Processing, vol. 27, no. 6, pp. 2828–2841, 2018.

[5] H. Lee, "Successive Low-light Image Enhancement Using an Image-adaptive Mask," Symmetry (Basel), vol. 14, no. 6, p. 1165, 2022.

[6] R. R. Hussein, Y. I. Hamodi and R. A. Sabri, "Retinex Theory for Color Image Enhancement: A Systematic Review," Int. J. Electr. Comput. Eng. (IJECE), vol. 9, no. 6, p. 5560, 2019.

[7] S. Hao, X. Han, Y. Guo, X. Xu and M. Wang, "Low-light Image Enhancement with Semi-decoupled Decomposition," IEEE Transactions on Multimedia, vol. 22, no. 12, pp. 3025–3038, 2020.

[8] M. A. Al-Hashim and Z. Al-Ameen, "Retinex-based Multiphase Algorithm for Low-light Image Enhancement," Traitement du Signal (TS), vol. 37, no. 5, pp. 733–743, 2020.

[9] W. Wang, Z. Chen, X. Yuan and X. Wu, "Adaptive Image Enhancement Method for Correcting Low-illumination Images," Information Sciences, vol. 496, pp. 25–41, 2019.

[10] Y. Ren, Z. Ying, T. H. Li and G. Li, "LECARM: Low-light Image Enhancement Using the Camera Response Model," IEEE Trans. on Circuits and Sys. for Video Techn., vol. 29, no. 4, pp. 968–981, 2019.

[11] M. Li, J. Liu, W. Yang, X. Sun and Z. Guo, "Structure-revealing Low-light Image Enhancement via Robust Retinex Model," IEEE Transactions on Image Processing, vol. 27, no. 6, pp. 2828–2841, 2018.

[12] X. Fu, D. Zeng, Y. Huang, Y. Liao, X. Ding and J. Paisley, "A Fusion-based Enhancing Method for Weakly Illuminated Images," Signal Processing, vol. 129, pp. 82–96, 2016.

[13] X. Guo, "LIME: A Method for Low-light Image Enhancement," Proc. of the 24th ACM Int. Conf. on Multimedia, DOI:10.1145/2964284.2967188, 2016.

[14] D. J. Jobson, Z. Rahman and G. A. Woodell, "Properties and Performance of a Center/Surround Retinex," IEEE Transactions on Image Processing, vol. 6, no. 3, pp. 451-462, 1997.

[15] Z. Rahman, D. J. Jobson, and G. A. Woodell, “Resiliency of the multiscale retinex image enhancement algorithm,” in Color Imaging Conference: Color Science, Systems and Applications, 1998.

[16] D. J. Jobson, "Retinex Processing for Automatic Image Enhancement," Journal of Electronic Imaging, vol. 13, no. 1, pp. 100-110, 2004.

[17] M. Ismail and Z. Al-Ameen, "Adapted Single Scale Retinex Algorithm for Nighttime Image Enhancement," AL-Rafidain J. of Computer Sciences and Mathematics, vol. 16, no. 1, pp. 59–69, 2022.

[18] Y. Meng, D. Kong, Z. Zhu and Y. Zhao, "From Night to Day: GANs Based Low Quality Image Enhancement," Neural Processing Letters, vol. 50, no. 1, pp. 799–814, 2019.

[19] R. C. Gonzalez and R. E. Woods, Digital Image Processing: International Edition, 3rd Ed., Upper Saddle River, NJ: Pearson, 2008.

[20] V. Patrascu and V. Buzuloiu, "Color Image Processing Using Logarithmic Operations," Proc. of the IEEE Int. Symposium on Signals, Circuits and Systems (SCS 2003), DOI: 10.1109/SCS.2003.1226966, 2004.

[21] R. J. Oosterbaan, "Software for Generalized and Composite Probability Distributions," International Journal of Mathematical and Computational Methods, vol. 4, no. 1, pp. 1-19, 2019.

[22] 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.

[23] E. Baidoo and K. Alex, "Implementation of Gray Level Image Transformation Techniques," Int. J. of Modern Education and Computer Science, vol. 10, no. 5, pp. 44–53, 2018.

[24] V. Bychkovsky, S. Paris, E. Chan and F. Durand, "Learning Photographic Global Tonal Adjustment with a Database of Input/Output Image Pairs," Proc. of the Int. Conf. on Computer Vision and Pattern Recognition (CVPR), DOI: 10.1109/CVPR.2011.5995413, Colorado Springs, USA, 2011.

[25] Y. P. Loh and C. S. Chan, "Getting to Know Low-light Images with the Exclusively Dark Dataset," Computer Vision and Image Understanding, vol. 178, pp. 30–42, 2019.

[26] M. R. Lone and A. K. Sandhu, "Enhancing Image Quality: A Nearest Neighbor Median Filter Approach for Impulse Noise Reduction," Multimedia Tools and Applications, pp. 1–17, 2023.

[27] S. Bao, S. Ma and C. Yang, “Multi-scale retinex-based contrast enhancement method for preserving the naturalness of color image,” Opt. Rev., vol. 27, no. 6, pp. 475–485, 2020.

[28] A. Mittal, R. Soundararajan and A. C. Bovik, "Making a ‘Completely Blind’ Image Quality Analyzer," IEEE Signal Processing Letters, vol. 20, no. 3, pp. 209–212, 2013.

[29] A. Mittal, A. K. Moorthy and A. C. Bovik, "No-reference Image Quality Assessment in the Spatial Domain," IEEE Transactions on Image Processing, vol. 21, no. 12, pp. 4695–4708, 2012.

[30] E. H. Land and J. J. McCann, "Lightness and Retinex Theory," J. of the Optical Society of America, vol.61, no. 1, pp. 1–11, 1971.

[31] W. Burger and M. Burge, Principles of Digital Image Processing: Fundamental Techniques, 1st Edn. London, England: Springer, 2009.

[32] X. Wu, B. Wu, J. He, B. Fang, Z. Shang and M. Zhou, "A Structure Preservation and Denoising Low-light Enhancement Model via Coefficient of Variation," Int. J. of Pattern Recognition and Artificial Intelligence, vol. 36, no. 13, DOI: 10.1142/S0218001422540180, 2022.

[33] 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," Visual Computer, vol. 39, no. 12, pp. 6755–6766, 2023.

[34] X. Yi, C. Min, M. Shao, H. Zheng and Q. Lv, "Low-light Image Enhancement via Regularized Gaussian Fields Model," Neural Processing Letters, vol. 55, no. 9, pp. 12017–12037, 2023.

[35] J. J. Jeon, J. Y. Park and I. K. Eom, "Low-light Image Enhancement Using Gamma Correction Prior in Mixed Color Spaces," Pattern Recognition, vol. 146, p. 110001, DOI: 10.1016/j.patcog.2023.110001, 2024.

[36] J. Li, X. Feng and Z. Hua, "Low-light Image Enhancement via Progressive-recursive Network," IEEE Transactions on Circuits and Systems for Video Technology, vol. 31, no. 11, pp. 4227–4240, 2021.

[37] D. Dai and L. Van Gool, "Dark Model Adaptation: Semantic Image Segmentation from Daytime to Nighttime," Proc. of the 21st Int. Conf. on Intelligent Transportation Systems (ITSC), DOI: 10.1109/ITSC.2018.8569387, Maui, USA, 2018.