Fetching the paper…
Reading the bibliography…
Images captured in the low-light condition suffer from low visibility and various imaging artifacts, e.g., real noise.
E. H. Land, “The retinex theory of color vision,” Scientific american , vol. 237, no. 6, pp. 108–129, 1977
1977
Earlier work this paper cites.
M. E. McCord, S. L. Bacharach, R. O. Bonow, V. Dilsizian, A. Cuocolo, and N. Freedman, “Misalignment between pet transmission and emission scans: its effect on myocardial imaging.” Journal of Nuclear Medicine: Official Publication, Society of Nuclear Medicine , vol. 33, no. 6, pp. 1209–14, 1992
1992
Earlier work this paper cites.
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 Transactions on Image processing , vol. 6, no. 7, pp. 965–976, 1997
1997
Earlier work this paper cites.
K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising with block-matching and 3d filtering,” in Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning , vol. 6064. International Society for Optics and Photonics, 2006, p. 606414
2006
Earlier work this paper cites.
M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Transactions on Image processing , vol. 15, no. 12, pp. 3736–3745, 2006
2006
Earlier work this paper cites.
M. Abdullah-Al-Wadud, M. H. Kabir, M. A. A. Dewan, and O. Chae, “A dynamic histogram equalization for image contrast enhancement,” IEEE Transactions on Consumer Electronics , vol. 53, no. 2, pp. 593–600, 2007
2007
Earlier work this paper cites.
K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Color image denoising via sparse 3d collaborative filtering with grouping constraint in luminance-chrominance space,” in IEEE International Conference on Image Processing (ICIP) , vol. 1. IEEE, 2007, pp. I–313
2007
Earlier work this paper cites.
S. Yamamoto, M. Maeda, N. Tsumura, T. Nakaguchi, R. Okamoto, Y. Miyake, and I. Shimoyama, “Subjective evaluation of visual fatigue due to misalignment of motion and still images in a stereoscopic display,” Journal of the Society for Information Display , vol. 20, no. 2, pp. 94–102, 2012
2012
Earlier work this paper cites.
B. Faiza, S. S. Yuhaniz, S. Z. M. Hashim, and K. K. AbdulRahman, “A review and analysis of image misalignment problem in remote sensing,” Int. J. Sci. Eng. Res. , p. 3, 2012
2012
Earlier work this paper cites.
C. Lee, C. Lee, and C.-S. Kim, “Contrast enhancement based on layered difference representation,” in IEEE International Conference on Image Processing (ICIP) . IEEE, 2012, pp. 965–968
2012
Earlier work this paper cites.
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
Earlier work this paper cites.
S. Gu, L. Zhang, W. Zuo, and X. Feng, “Weighted nuclear norm minimization with application to image denoising,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2014, pp. 2862–2869
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” Advances in Neural Information Processing Systems (NeurIPS) , vol. 27, 2014
2014
Earlier work this paper cites.
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 (CVPR) , 2015, pp. 3431–3440
2015
Earlier work this paper cites.
M. Jaderberg, K. Simonyan, A. Zisserman et al. , “Spatial transformer networks,” Advances in Neural Information Processing Systems (NeurIPS) , vol. 28, pp. 2017–2025, 2015
2015
Earlier work this paper cites.
K. Ma, K. Zeng, and Z. Wang, “Perceptual quality assessment for multi-exposure image fusion,” IEEE Transactions on Image Processing , vol. 24, no. 11, pp. 3345–3356, 2015
2015
Earlier work this paper cites.
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
Earlier work this paper cites.
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
Earlier work this paper cites.
2016
Earlier work this paper cites.
2016
Cited alongside, same era.
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
Cited alongside, same era.
M. Gharbi, J. Chen, J. T. Barron, S. W. Hasinoff, and F. Durand, “Deep bilateral learning for real-time image enhancement,” ACM Transactions on Graphics (TOG) , vol. 36, no. 4, p. 118, 2017
2017
Cited alongside, same era.
C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang et al. , “Photo-realistic single image super-resolution using a generative adversarial network,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2017, pp. 4681–4690
2017
Cited alongside, same era.
X. Zhang, Q. Chen, R. Ng, and V. Koltun, “Zoom to learn, learn to zoom,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 3762–3770
2019
Later among the works it cites.
C. Guo, C. Li, J. Guo, C. C. Loy, J. Hou, S. Kwong, and R. Cong, “Zero-reference deep curve estimation for low-light image enhancement,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2020, pp. 1780–1789
2020
Later among the works it cites.
B. Xu, L. He, X. Liao, W. Liu, Z. Sun, and T. Mei, “Black Re-ID: A head-shoulder descriptor for the challenging problem of person re-identification,” in Proceedings of the 28th ACM International Conference on Multimedia , 2020, pp. 673–681
2020
Later among the works it cites.
Z. Zheng, Y. Wu, X. Han, and J. Shi, “Forkgan: Seeing into the rainy night,” in Proceedings of the European Conference on Computer Vision (ECCV) , August 2020
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV) , 2017, pp. 2223–2232
2017
Cited alongside, same era.
M.-Y. Liu, T. Breuel, and J. Kautz, “Unsupervised image-to-image translation networks,” Advances in Neural Information Processing Systems (NeurIPS) , vol. 30, pp. 700–708, 2017
2017
Cited alongside, same era.
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
Cited alongside, same era.
W. Chen, W. Wenjing, Y. Wenhan, and L. Jiaying, “Deep retinex decomposition for low-light enhancement,” in British Machine Vision Conference . British Machine Vision Association, 2018
2018
Cited alongside, same era.
X. Huang, M.-Y. Liu, S. Belongie, and J. Kautz, “Multimodal unsupervised image-to-image translation,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 172–189
2018
Cited alongside, same era.
H.-Y. Lee, H.-Y. Tseng, J.-B. Huang, M. Singh, and M.-H. Yang, “Diverse image-to-image translation via disentangled representations,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 35–51
2018
Cited alongside, same era.
2018
Cited alongside, same era.
D. Liu, B. Wen, Y. Fan, C. C. Loy, and T. S. Huang, “Non-local recurrent network for image restoration,” in Advances in Neural Information Processing Systems (NeurIPS) , 2018, pp. 1673–1682
2018
Cited alongside, same era.
T. Park, A. A. Efros, R. Zhang, and J.-Y. Zhu, “Contrastive learning for unpaired image-to-image translation,” in Proceedings of the European Conference on Computer Vision (ECCV) . Springer, 2020, pp. 319–345
2020
Later among the works it cites.
W. Yang, Y. Yuan, W. Ren, J. Liu, W. J. Scheirer, Z. Wang, and Zhang, “Advancing image understanding in poor visibility environments: A collective benchmark study,” IEEE Transactions on Image Processing , vol. 29, pp. 5737–5752, 2020
2020
Later among the works it cites.
X. Ren, W. Yang, W.-H. Cheng, and J. Liu, “Lr3m: Robust low-light enhancement via low-rank regularized retinex model,” IEEE Transactions on Image Processing , vol. 29, pp. 5862–5876, 2020
2020
Later among the works it cites.
S. W. Zamir, A. Arora, S. Khan, M. Hayat, F. S. Khan, M.-H. Yang, and L. Shao, “Learning enriched features for real image restoration and enhancement,” in ECCV , 2020
2020
Later among the works it cites.
W. Yang, S. Wang, Y. Fang, Y. Wang, and J. Liu, “From fidelity to perceptual quality: A semi-supervised approach for low-light image enhancement,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2020
2020
Later among the works it cites.
L. Kong, C. Lian, D. Huang, Z. Li, Y. Hu, and Q. Zhou, “Breaking the dilemma of medical image-to-image translation,” in Thirty-Fifth Conference on Neural Information Processing Systems , 2021. [Online]. Available: https://openreview.net/forum?id=C0GmZH2RnVR
2021
Later among the works it cites.
Y. Jiang, X. Gong, D. Liu, Y. Cheng, C. Fang, X. Shen, J. Yang, P. Zhou, and Z. Wang, “Enlightengan: Deep light enhancement without paired supervision,” IEEE Transactions on Image Processing , vol. 30, pp. 2340–2349, 2021
2021
Later among the works it cites.
R. Liu, Y. Ge, C. L. Choi, X. Wang, and H. Li, “Divco: Diverse conditional image synthesis via contrastive generative adversarial network,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2021, pp. 16 377–16 386
2021
Later among the works it cites.
C. Li, C. Guo, L. Han, J. Jiang, M.-M. Cheng, J. Gu, and C. C. Loy, “Low-light image and video enhancement using deep learning: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2021
2021
Later among the works it cites.
L. Risheng, M. Long, Z. Jiaao, F. Xin, and L. Zhongxuan, “Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2021
2021
Later among the works it cites.
L. Ma, R. Liu, J. Zhang, X. Fan, and Z. Luo, “Learning deep context-sensitive decomposition for low-light image enhancement,” IEEE Transactions on Neural Networks and Learning Systems , 2021
2021
Later among the works it cites.
L. Zhao, S.-P. Lu, T. Chen, Z. Yang, and A. Shamir, “Deep symmetric network for underexposed image enhancement with recurrent attentional learning,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , October 2021, pp. 12 075–12 084
2021
Later among the works it cites.
F. Lv, Y. Li, and F. Lu, “Attention guided low-light image enhancement with a large scale low-light simulation dataset,” International Journal of Computer Vision , vol. 129, no. 7, pp. 2175–2193, 2021
2021
Later among the works it cites.
Y. Zhang, X. Guo, J. Ma, W. Liu, and J. Zhang, “Beyond brightening low-light images,” International Journal of Computer Vision , vol. 129, no. 4, pp. 1013–1037, 2021
2021
Later among the works it cites.