Improvement of the Sharpness and Brightness of Dim Images Using the Retinax Approach and Nonlinear Conversion
Subject Areas : electrical and computer engineering
maryam
ghasemi
1
(ferdowsi university of mashhad)
Morteza
Khademi
2
(Ferdowsi University of mashhad)
Abbas
Ebrahimi moghadam
3
(Ferdowsi University of mashhad)
Keywords: Improved dim images, improved brightness and sharpness, nonlinear conversion, RETINEX-based methods,
Abstract :
Images captured in low light conditions are unsuitable for human and machine vision due to low brightness and sharpness and high noise, and have a negative effect on their performance. Much research has been done to improve such images. The methods proposed so far to solve this problem greatly improve such images. One of these methods is the RETINEX-based method, which modifies low-light images, but because the initial structure of this method is complex and inefficient, researchers have developed other methods such as SSR, MSR, and MSRCR. To solve the problem, they have presented this approach. These methods, in turn, have problems such as abnormal images and amplification of noise. In the continuation of the work done, the field of optimization has been used, which shows better performance than the previous works. In this research, by obtaining the optimal brightness component, using nonlinear conversion and applying smoothing filter and reducing noise on the image as a post-processing step, these weaknesses are largely eliminated. By applying the proposed method, the resulting images look more natural and their information is more preserved. Subjective and objective criteria such as EI, SSIM, PSNR and IMMSE were used to evaluate the proposed method. The simulation results show the superiority of the proposed method over the competing methods.
[1] Y. Xu, C. Yang, B. Sun, X. Yan, and M. Chen, "A novel multi-scale fusion framework for detail-preserving low-light image enhancement," Information Sciences, vol. 548, pp. 378-397, Feb. 2021.
[2] 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.
[3] X. Liu, G. Cheung, X. Ji, D. Zhao, and W. Gao, "Graph-based joint dequantization and contrast enhancement of poorly lit JPEG images," IEEE Trans. on Image Processing, vol. 28, no. 3, pp. 1205-1219, Mar. 2018.
[4] S. Parihar and K. Singh, "A study on retinex based method for image enhanceme," in Proc. of the 2nd Int. Conf. on Inventive Systems and Control, ICISC'18, pp. 619-624, Coimbatore, India, 19-20 Jan. 2018.
[5] A. Mahmood, S. A. Khan, S. Hussain, and E. M. Almaghayreh, "An adaptive image contrast enhancement technique for low-contrast images," IEEE Access, vol. 7, pp. 161584-161593, 2019.
[6] T. Dong, G. Zhao, J. Wu, Y. Ye, and Y. Shen, "Efficient traffic video dehazing using adaptive dark channel prior and spatial-temporal correlations," Sensors, vol. 19, no. 7, Artucle ID: 1593, 2019.
[7] Y. Chang, C. Jung, P. Ke, H. Song, and J. Hwang, "Automatic contrast-limited adaptive histogram equalization with dual gamma correction," IEEE Access, vol. 6, pp. 11782-11792, 2018.
[8] J. Xiong, et al., "Application of histogram equalization for image enhancement in corrosion areas," Shock and Vibration, vol. 2021, Article ID: 8883571, 2021.
[9] P. P. Banik, R. Saha, and K. D. Kim, "Contrast enhancement of low-light image using histogram equalization and illumination adjustment," in Proc. IEEE Int. Conf. on Electronics, Information, and Communication, ICEIC'18, 4 pp., Honolulu, HI, USA, 24-27 Jan. 2018.
[10] K. A. Dar and S. Mittal, "An enhanced adaptive histogram equalization based local contrast preserving technique for HDR images," in Proc. IOP Conf. Series: Materials Science and Engineering, vol. 1022, Article: ID: 012119, 12 pp., 2021.
[11] E. H. Land and J. J. McCann, "Lightness and retinex theory," Journal of the Optical Society of America, vol. 61, no. 1, pp. 1-11, 1971.
[12] Y. Guo, Y. Lu, R. W. Liu, M. Yang, and K. T. Chui, "Low-light image enhancement with regularized illumination optimization and deep noise suppression," IEEE Access, vol. 8, pp. 145297-145315, 2020.
[13] H. Tanaka, Y. Waizumi, and T. Kasezawa, "Retinex-based signal enhancement for image dark regions," in Proc. IEEE Int, Conf. on Signal and Image Processing Applications, ICSIPA'17, pp. 205-209, Kuching, Malaysia, 12-14 Sept. 2017.
[14] J. Zhou, D. Zhang, P. Zou, W. Zhang, and W. Zhang, "Retinex-based laplacian pyramid method for image defogging," IEEE Access, vol. 7, pp. 122459-122472, 2019.
[15] S. Park, K. Kim, S. Yu, and J. Paik, "Contrast enhancement for low-light image enhancement: a survey," IEIE Trans. on Smart Processing & Computing, vol. 7, pp. 36-48, 2018.
[16] K. A. Dar and S. Mittal, "A dynamic fuzzy histogram equalization for high dynamic range images by using multi-scale retinex algorithm," in Proc. of the First Int. Conf. on Innovative Computing & Communications, ICICC'20, 8 pp., 1-1 Apr. 2020.
[17] P. Hao, S. Wang, S. Li, and M. Yang, "Low-light image enhancement based on retinex and saliency theories," in Proc.Chinese Automation Congress, CAC'19, pp. 2594-2597, Hangzhou, China, 22-24 Nov. 2019.
[18] M. Cornia, L. Baraldi, G. Serra, and R. Cucchiara, "Predicting human eye fixations via an LSTM-based saliency attentive model," IEEE Trans. on Image Processing, vol. 27, no. 10, pp. 5142-5154, Oct. 2018.
[19] M. A. Al-Hashim and Z. Al-Ameen, "Retinex-based multiphase algorithm for low-light image enhancement," Traitement du Signal, vol. 37, no. 5, pp. 733-743, Oct. 2020.
[20] S. Liu, W. Long, L. He, Y. Li, and W. Ding, "Retinex-based fast algorithm for low-light image enhancement entropy," Entropy, vol. 21, no. 6, Article ID: 746, 2021.
[21] A. Arora, et al., Low Light Image Enhancement via Global and Local Context Modeling, arXiv preprint arXiv: 2101.00850, Jan. 2021.
[22] H. Liang, A. Yu, M. Shao, and Y. Tian, "Multi-feature guided low-light image enhancement," Applied Sciences, vol. 11, Article ID: 5055, 11 pp., 2021.
[23] X. Guo, Y. Li, and H. Ling, "LIME: low-light image enhancement via illumination map estimation," IEEE Trans. on Image Processing, vol. 26, no. 2, pp. 982-993, Feb. 2017.
[24] S. Rahman, et al., "An adaptive gamma correction for image enhancement," EURASIP J. on Image and Video Processing, vol. 26, Article ID: 35, 13 pp., 2016.
[25] Wikipedia, Guided Filter, last edited in 2021, https://en.wikipedia.org/wiki/Guided_filter.
[26] X. Fu, D. Zeng, Y. Huang, X. P. Zhang, and X. Ding, "A weighted variational model for simultaneous reflectance and illumination estimation," in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 2782-2790, Las Vegas, NV, USA, 27-30 Jun. 2016.
[27] S. C. Huang, F. C. Cheng, and Y. S. Chiu, "Efficient contrast enhancement using adaptive gamma correction with weighting distribution," IEEE Trans. on Image Processing, vol. 22, no. 3, pp. 1032-1041, Mar. 2013.
[28] X. Fu, et al., "A fusion-based enhancing method for weakly illuminated images," Signal Processing, vol. 129, no. C, pp. 82-96, Dec. 2016.
[29] L. Li, S. Sun, C. Xia, P. Chen, and F. M. Dong, "Survey of histogram equalization technology," Comput. Syst. Appl, vol. 23, no. 3, pp. 1-8, 2014.