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This paper proposes a self-supervised low light image enhancement method based on deep learning.
G. Fu, L. Duan, and C. Xiao, “A hybrid l2-lp 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
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Y.-T. Kim, “Contrast enhancement using brightness preserving bi-histogram equalization,” IEEE transactions on Consumer Electronics , vol. 43, no. 1, pp. 1–8, 1997
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D. J. Jobson, Z.-u. 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
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Y. Wang, Q. Chen, and B. Zhang, “Image enhancement based on equal area dualistic sub-image histogram equalization method,” IEEE Transactions on Consumer Electronics , vol. 45, no. 1, pp. 68–75, 1999
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S. K. Naik and C. Murthy, “Hue-preserving color image enhancement without gamut problem,” IEEE Transactions on Image Processing , vol. 12, no. 12, pp. 1591–1598, 2003
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R. Kimmel, M. Elad, D. Shaked, R. Keshet, and I. Sobel, “A variational framework for retinex,” International Journal of computer vision , vol. 52, no. 1, pp. 7–23, 2003
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T. Celik and T. Tjahjadi, “Contextual and variational contrast enhancement,” IEEE Transactions on Image Processing , vol. 20, no. 12, pp. 3431–3441, 2011
2011
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X. Dong, G. Wang, Y. Pang, W. Li, J. Wen, W. Meng, and Y. Lu, “Fast efficient algorithm for enhancement of low lighting video,” in 2011 IEEE International Conference on Multimedia and Expo . IEEE, 2011, pp. 1–6
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R. L. Lagendijk and J. Biemond, Iterative identification and restoration of images . Springer Science & Business Media, 2012, vol. 118
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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, 2012
2012
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C. Lee, C. Lee, and C.-S. Kim, “Contrast enhancement based on layered difference representation of 2d histograms,” IEEE transactions on image processing , vol. 22, no. 12, pp. 5372–5384, 2013
2013
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S. Wang, J. Zheng, H.-M. Hu, and B. Li, “Naturalness preserved enhancement algorithm for non-uniform illumination images,” IEEE Transactions on Image Processing , vol. 22, no. 9, pp. 3538–3548, 2013
2013
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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 . IEEE, 2013, pp. 1085–1088
2013
Cited alongside, same era.
2014
Cited alongside, same era.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” in Advances in neural information processing systems , 2015, pp. 91–99
2015
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J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 3431–3440
2015
Cited alongside, same era.
S. Park, S. Yu, B. Moon, S. Ko, and J. Paik, “Low-light image enhancement using variational optimization-based retinex model,” IEEE Transactions on Consumer Electronics , vol. 63, no. 2, pp. 178–184, 2017
2017
Later among the works it cites.
K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, “Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising,” IEEE Transactions on Image Processing , vol. 26, no. 7, pp. 3142–3155, 2017
2017
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S. Nah, T. Hyun Kim, and K. Mu Lee, “Deep multi-scale convolutional neural network for dynamic scene deblurring,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 3883–3891
2017
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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
2017
Later among the works it cites.
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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, 2015
2015
Cited alongside, same era.
L. Li, R. Wang, W. Wang, and W. Gao, “A low-light image enhancement method for both denoising and contrast enlarging,” in 2015 IEEE International Conference on Image Processing (ICIP) . IEEE, 2015, pp. 3730–3734
2015
Cited alongside, same era.
C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE transactions on pattern analysis and machine intelligence , vol. 38, no. 2, pp. 295–307, 2015
2015
Cited alongside, same era.
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 Transactions on Image Processing , vol. 24, no. 12, pp. 4965–4977, 2015
2015
Cited alongside, same era.
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in European conference on computer vision . Springer, 2016, pp. 21–37
2016
Cited alongside, same era.
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
2016
Cited alongside, same era.
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, 2016
2016
Cited alongside, same era.
J. Yang, X. Jiang, C. Pan, and C.-L. Liu, “Enhancement of low light level images with coupled dictionary learning,” in 2016 23rd International Conference on Pattern Recognition (ICPR) . IEEE, 2016, pp. 751–756
2016
Cited alongside, same era.
2017
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2018
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C. Chen, Q. Chen, J. Xu, and V. Koltun, “Learning to see in the dark,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 3291–3300
2018
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2018
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S. Park, S. Yu, M. Kim, K. Park, and J. Paik, “Dual autoencoder network for retinex-based low-light image enhancement,” IEEE Access , vol. 6, pp. 22 084–22 093, 2018
2018
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O. Kupyn, V. Budzan, M. Mykhailych, D. Mishkin, and J. Matas, “Deblurgan: Blind motion deblurring using conditional adversarial networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 8183–8192
2018
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C. Li, J. Guo, F. Porikli, and Y. Pang, “Lightennet: A convolutional neural network for weakly illuminated image enhancement,” Pattern Recognition Letters , vol. 104, pp. 15–22, 2018
2018
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J. Cai, S. Gu, and L. Zhang, “Learning a deep single image contrast enhancer from multi-exposure images,” IEEE Transactions on Image Processing , vol. 27, no. 4, pp. 2049–2062, 2018
2018
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2018
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S. Guo, Z. Yan, K. Zhang, W. Zuo, and L. Zhang, “Toward convolutional blind denoising of real photographs,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 1712–1722
2019
Later among the works it cites.
Z. Li, J. Yang, Z. Liu, X. Yang, G. Jeon, and W. Wu, “Feedback network for image super-resolution,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 3867–3876
2019
Later among the works it cites.
Y. Zhang, J. Zhang, and X. Guo, “Kindling the darkness: A practical low-light image enhancer,” in Proceedings of the 27th ACM International Conference on Multimedia , 2019, pp. 1632–1640
2019
Later among the works it cites.