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http://dx.doi.org/10.7471/ikeee.2021.25.1.139

Low-light Image Enhancement Method Using Decomposition-based Deep-Learning  

Oh, Jong-Geun (Dept. of Electronics and information Engineering, Soongsil University)
Hong, Min-Cheol (Dept. of Electronics and information Engineering, Soongsil University)
Publication Information
Journal of IKEEE / v.25, no.1, 2021 , pp. 139-147 More about this Journal
Abstract
This paper introduces an image decomposition-based deep learning method and loss function to improve low-light images. In order to remove color distortion and halo artifact, illuminance channel of an input image is decomposed into reflectance and luminance channels, and a decomposition-based multiple structural deep learning process is applied to each channel. In addition, a mixed norm-based loss function is described to increase the stability and remove blurring in reconstructed image. Experimental results show that the proposed method effectively improve various low-light images.
Keywords
image enhancement; image decomposition; deep-learning; Retinex model; mixed-norm;
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1 Y. Shin, S. Jeong, and S. Lee. "Efficient naturalness restoration for non-uniform illumination images," IET Image Process., vol.9, no.9, pp.662-671, 2015.   DOI
2 M. Lecca, A. Rizzi, and R. P. Serapioni. "GRASS: A gradient-based random sampling scheme for Milano retinex," IEEE Trans. on Image Process., vol.26, no.6, pp.2767-2780, 2017. DOI: 10.1109/TIP.2017.2686652   DOI
3 R. Kimmel, M. Elad, D. Shaked, and I. Sobel. "A variational framework for retinex," Int. J. of Comput. Vis., vol.52, pp.7-23, 2003. DOI: 10.1023/A:1022314423998   DOI
4 D. Zosso, G. Tran, and S. J. Osher. "Non-local retinex-A unifying framework and beyond," SIAM J. on Image Sci., vol.8, no.2, pp.787-826, 2015. DOI: 10.1023/A:1022314423998   DOI
5 S. Park, S. Yu, B. Moon, S. Ko, and J. Paik, "Low-light image enhancement using variational optimization-based retinex model," IEEE Trans. on Consumer Electron., vol.63, no.2, pp.178-184, 2017. DOI: 10.1109/TCE.2017.014847   DOI
6 K. G. Lore, A. Akintayo, and S. Sarkar. "LLNet: A deep autoencoder approach to natural low-light image enhancement," Pattern Recognition, vol.61, pp.650-662, 2017. DOI: 10.1016/j.patcog.2016.06.008   DOI
7 L. Shen, Z. Yue, F. Feng, Q. Chen, S. Liu, and J. Ma. "Msr-net: lowlight image enhancement using deep convolutional network," arXiv:1711.02488 [cs.CV], 2017.
8 X. Guo, Y. Li, and H. Ling. "Lime: Low-light image enhancement via illumination map estimation," IEEE Trans. on Image Process., vol.26, no.2, pp.982-993, 2017. DOI: 10.1109/TIP.2016.2639450   DOI
9 C. Wei, W. Wang, W. Yang, and J. Liu. "Deep retinex decomposition for low-light enhancement," arXiv:1808.04560 [cs.CV], 2018.
10 F. Lv, F. Lu, J. Wu, and C. Lim, "MBLLEn: Low-light image/video enhancement using CNNs," Proc. Brit. Mach. Vis. Conf. (BMVC), pp.1-13, 2019.
11 Y. Zhang, J. Zhang, and X. Guo, "Kindling the darkness: A practical low-light image enhancer," Proc. of ACM Int. Conf. on Mult., pp.1632-1640, 2019. DOI: 10.1145/3343031.3350926   DOI
12 J. G. Oh, M.-C. Hong. "Adaptive image rendering using a nonlinear mapping function based retinex model," Sensors, vol.19, No.4, pp.969, 2019. DOI: 10.3390/s19040969   DOI
13 D. P. Kingma and J. Ba, "Adam: A method for stochastic optimization," arViv:1412.6980v9 [cs.LG], 2017.
14 H. R. Sheikh, Z. Wang, L. Cormack, and A. C. Bovik. "Live image quality assessment database release 2," [Online] Available: https://live.ece.utexas.edu/research/quality.
15 S. Wang, J. Zheng, H. Hu, and B. Li. "Naturalness preserved enhancement algorithm for non-uniform illumination images," IEEE Trans. on Image Process,, no.22, pp.3538-3548, 2013. DOI: 10.1109/TIP.2013.2261309   DOI
16 Stanford Vision Lab. "ImageNet." [Online] Available: http://image-net.org/about-overview.
17 NASA/Langley Research Center/Electromagnetics and Sensors Research Branch. [Online] Available: https://dragon.larc.nasa.gov.
18 P. Arbelaez, C. Fowlkes, and D. Martin, "The Berkeley Segmentation Dataset and Benchmark," [Online] Available: https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds.
19 A. Mittal, R. Soundararajan, and A. C. Bovik. "Making a "completely blind" image quality analyzer," IEEE Signal Process. Letters, no.20, pp.209-212, 2013. DOI: 10.1109/LSP.2012.2227726   DOI
20 W. Wang, X. Wu, X. Yuan, and Z. Gao, "An experiment-based review of low-light image enhancement method," IEEE Access, vol.8, pp. 87884-87917, 2020. DOI: 10.1109/ACCESS.2020.2992749   DOI
21 E. H. Land and J. J. McCann. "Lightness and retinex theory," J. of Opt. Soc. of America, vol.61, no.1, pp.1-11, 1971. DOI: 10.1364/JOSA.61.000001   DOI
22 D. J. Jobson, Z. Rahman and G. A. Woodell. "Properties and performance of a center/surround retinex," IEEE Trans. on Image Process., vol.6, no.3, pp.451-461, 1997. DOI: 10.1109/83.557356   DOI
23 D. J. Jobson, Z. Rahman and G. A. Woodell. "A multi-scale retinex for bridging the gap between color images and the human observation of scenes," IEEE Trans. on Image Process., vol.6, no.7, pp. 965-976, 1997. DOI: 10.1109/83.597272   DOI
24 E. Provenzi, M. Fierro, A. Rizzi, L. D. Carli, D. Gadia, and D. Marini. "Random spray retinex: A new retinex implementation to investigate the local properties of the model," IEEE Trans. on Image Process., vol.16, no.1, pp.162-171, 2007. DOI: 10.1109/tip.2006.884946   DOI
25 T. Celik. "Spatial entropy-based global and local image contrast enhancement," IEEE Trans. on Image Process., vol.23, no.12, pp.5298-5308, 2014. DOI: 10.1109/TIP.2014.2364537   DOI