Fetching the paper…
Reading the bibliography…
Images captured in challenging environments--such as nighttime, smoke, rainy weather, and underwater--often suffer from significant degradation, resulting in a substantial loss of visual quality.
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.
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Transactions on Image Processing , vol. 13, no. 4, pp. 600–612, 2004
2004
Earlier work this paper cites.
A. Hore and D. Ziou, “Image quality metrics: Psnr vs. ssim,” in Proceedings of the International Conference on Pattern Recognition , 2010, pp. 2366–2369
2010
Earlier work this paper cites.
K. He, J. Sun, and X. Tang, “Single image haze removal using dark channel prior,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 33, no. 12, pp. 2341–2353, 2010
2010
Earlier work this paper cites.
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
Earlier work this paper cites.
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
Earlier work this paper cites.
2014
Earlier work this paper cites.
Y. Li, R. T. Tan, and M. S. Brown, “Nighttime haze removal with glow and multiple light colors,” in Proceedings of the IEEE International Conference on Computer Vision , 2015, pp. 226–234
2015
Earlier work this paper cites.
M. Yang and A. Sowmya, “An underwater color image quality evaluation metric,” IEEE Transactions on Image Processing , vol. 24, no. 12, pp. 6062–6071, 2015
2015
Earlier work this paper cites.
X. Fu, J. Huang, X. Ding, Y. Liao, and J. W. Paisley, “Clearing the skies: A deep network architecture for single-image rain removal,” IEEE Transactions on Image Processing , vol. 26, pp. 2944–2956, 2016
2016
Earlier work this paper cites.
W. Yang, R. T. Tan, J. Feng, J. Liu, Z. Guo, and S. Yan, “Deep joint rain detection and removal from a single image,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 1685–1694, 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.
C. Ancuti, C. O. Ancuti, C. De Vleeschouwer, and A. C. Bovik, “Night-time dehazing by fusion,” in Proceedings of the IEEE International Conference on Image Processing (ICIP) , 2016, pp. 2256–2260
2016
Earlier work this paper cites.
A. Ignatov, N. Kobyshev, R. Timofte, K. Vanhoey, and L. Van Gool, “Dslr-quality photos on mobile devices with deep convolutional networks,” in Proceedings of the IEEE International Conference on Computer Vision , 2017, pp. 3277–3285
2017
Earlier work this paper cites.
J. Zhang, Y. Cao, S. Fang, Y. Kang, and C. Wen Chen, “Fast haze removal for nighttime image using maximum reflectance prior,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 7418–7426
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
H. Zhu, P. Xi, V. Chandrasekhar, L. Li, and J. H. Lim, “Dehazegan: When image dehazing meets differential programming,” in Proceedings of the International Joint Conference on Artificial Intelligence , 2018, pp. 1234–1240
2018
Earlier work this paper cites.
H. Zhang and V. M. Patel, “Density-aware single image de-raining using a multi-stream dense network,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pp. 695–704, 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
S. Woo, J. Park, J.-Y. Lee, and I. S. Kweon, “Cbam: Convolutional block attention module,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 3–19
2018
Earlier work this paper cites.
Y.-T. Peng, K. Cao, and P. C. Cosman, “Generalization of the dark channel prior for single image restoration,” IEEE Transactions on Image Processing , vol. 27, no. 6, pp. 2856–2868, 2018
2018
Earlier work this paper cites.
C. Fabbri, M. J. Islam, and J. Sattar, “Enhancing underwater imagery using generative adversarial networks,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) , 2018, pp. 7159–7165
2018
Earlier work this paper cites.
W. Wang, C. Wei, W. Yang, and J. Liu, “Gladnet: Low-light enhancement network with global awareness,” in Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition , 2018, pp. 751–755
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The unreasonable effectiveness of deep features as a perceptual metric,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 586–595
2018
Earlier work this paper cites.
C. Wei, W. Wang, W. Yang, and J. Liu, “Deep retinex decomposition for low-light enhancement,” in Proceedings of the British Machine Vision Conference , 2019, pp. 1–11
2019
Earlier work this paper cites.
D. Ren, W. Zuo, Q. Hu, P. F. Zhu, and D. Meng, “Progressive image deraining networks: A better and simpler baseline,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 3932–3941, 2019
2019
Earlier work this paper cites.
R. Yasarla and V. M. Patel, “Uncertainty guided multi-scale residual learning-using a cycle spinning cnn for single image de-raining,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 8397–8406, 2019
2019
Earlier work this paper cites.
W. Yang, R. T. Tan, J. Feng, Z. Guo, S. Yan, and J. Liu, “Joint rain detection and removal from a single image with contextualized deep networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 42, no. 6, pp. 1377–1393, 2019
2019
Earlier work this paper cites.
C. Li, C. Guo, W. Ren, R. Cong, J. Hou, S. Kwong, and D. Tao, “An underwater image enhancement benchmark dataset and beyond,” IEEE Transactions on Image Processing , vol. 29, pp. 4376–4389, 2019
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
R. Wang, Q. Zhang, C.-W. Fu, X. Shen, W.-S. Zheng, and J. Jia, “Underexposed photo enhancement using deep illumination estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 6849–6857
2019
Earlier work this paper cites.
Y. Zhang, J. Zhang, and X. Guo, “Kindling the darkness: A practical low-light image enhancer,” in Proceedings of the ACM International Conference on Multimedia , 2019, pp. 1632–1640
2019
Earlier work this paper cites.
X. Liu, Y. Ma, Z. Shi, and J. Chen, “Griddehazenet: Attention-based multi-scale network for image dehazing,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 7314–7323
2019
Cited alongside, same era.
W. Yan, R. T. Tan, and D. Dai, “Nighttime defogging using high-low frequency decomposition and grayscale-color networks,” in Proceedings of the European Conference on Computer Vision , 2020, pp. 473–488
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Q. Wang, B. Wu, P. Zhu, P. Li, W. Zuo, and Q. Hu, “Eca-net: Efficient channel attention for deep convolutional neural networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 534–11 542
2020
Cited alongside, same era.
C.-L. Guo, Q. Yan, S. Anwar, R. Cong, W. Ren, and C. Li, “Image dehazing transformer with transmission-aware 3d position embedding,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 5812–5820
2022
Later among the works it cites.
Y. Jin, B. Lin, W. Yan, Y. Yuan, W. Ye, and R. T. Tan, “Enhancing visibility in nighttime haze images using guided apsf and gradient adaptive convolution,” in Proceedings of the ACM International Conference on Multimedia , 2023, pp. 2446–2457
2023
Later among the works it cites.
2023
Later among the works it cites.
L. Peng, C. Zhu, and L. Bian, “U-shape transformer for underwater image enhancement,” IEEE Transactions on Image Processing , vol. 32, pp. 3066–3079, 2023
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
H. Zhao, X. Kong, J. He, Y. Qiao, and C. Dong, “Efficient image super-resolution using pixel attention,” in Proceedings of the European Conference on Computer Vision , 2020, pp. 56–72
2020
Cited alongside, same era.
K. Jiang, Z. Wang, P. Yi, C. Chen, B. Huang, Y. Luo, J. Ma, and J. Jiang, “Multi-scale progressive fusion network for single image deraining,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 8346–8355
2020
Cited alongside, same era.
C. Li, S. Anwar, and F. Porikli, “Underwater scene prior inspired deep underwater image and video enhancement,” Pattern Recognition , vol. 98, p. 107038, 2020
2020
Cited alongside, same era.
M. J. Islam, Y. Xia, and J. Sattar, “Fast underwater image enhancement for improved visual perception,” IEEE Robotics and Automation Letters , vol. 5, no. 2, pp. 3227–3234, 2020
2020
Cited alongside, same era.
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/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 1780–1789
2020
Cited alongside, same era.
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/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 3063–3072
2020
Cited alongside, same era.
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 Proceedings of the European Conference on Computer Vision , 2020, pp. 492–511
2020
Cited alongside, same era.
H. Dong, J. Pan, L. Xiang, Z. Hu, X. Zhang, F. Wang, and M.-H. Yang, “Multi-scale boosted dehazing network with dense feature fusion,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 2157–2167
2020
Cited alongside, same era.
2023
Later among the works it cites.
C. Guo, R. Wu, X. Jin, L. Han, W. Zhang, Z. Chai, and C. Li, “Underwater ranker: Learn which is better and how to be better,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, 2023, pp. 702–709
2023
Later among the works it cites.
2023
Later among the works it cites.
Z. Luo, F. K. Gustafsson, Z. Zhao, J. Sjölund, and T. B. Schön, “Image restoration with mean-reverting stochastic differential equations,” in Proceedings of the International Conference on Machine Learning , 2023, pp. 23 045–23 066
2023
Later among the works it cites.
Z. Hao, S. Gai, and P. Li, “Multi-scale self-calibrated dual-attention lightweight residual dense deraining network based on monogenic wavelets,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 33, no. 6, pp. 2642–2655, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
Y. Cui, Y. Tao, Z. Bing, W. Ren, X. Gao, X. Cao, K. Huang, and A. Knoll, “Selective frequency network for image restoration,” in Proceedings of the International Conference on Learning Representations , 2023
2023
Later among the works it cites.
B. Fei, Z. Lyu, L. Pan, J. Zhang, W. Yang, T. Luo, B. Zhang, and B. Dai, “Generative diffusion prior for unified image restoration and enhancement,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 9935–9946
2023
Later among the works it cites.
Y. Tang, H. Kawasaki, and T. Iwaguchi, “Underwater image enhancement by transformer-based diffusion model with non-uniform sampling for skip strategy,” in Proceedings of the ACM International Conference on Multimedia , 2023, pp. 5419–5427
2023
Later among the works it cites.
Z. Luo, F. K. Gustafsson, Z. Zhao, J. Sjölund, and T. B. Schön, “Refusion: Enabling large-size realistic image restoration with latent-space diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops , 2023, pp. 1680–1691
2023
Later among the works it cites.
O. Özdenizci and R. Legenstein, “Restoring vision in adverse weather conditions with patch-based denoising diffusion models,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 45, no. 8, pp. 10 346–10 357, 2023
2023
Later among the works it cites.
Y. Jin, B. Lin, W. Yan, Y. Yuan, W. Ye, and R. T. Tan, “Enhancing visibility in nighttime haze images using guided apsf and gradient adaptive convolution,” in Proceedings of the ACM International Conference on Multimedia , 2023, pp. 2446–2457
2023
Later among the works it cites.
Y. Song, Z. He, H. Qian, and X. Du, “Vision transformers for single image dehazing,” IEEE Transactions on Image Processing , vol. 32, pp. 1927–1941, 2023
2023
Later among the works it cites.
J. Liu, Q. Wang, H. Fan, Y. Wang, Y. Tang, and L. Qu, “Residual denoising diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 2773–2783
2024
Closest in time.
Y. Liu, X. Chen, X. Ma, X. Wang, J. Zhou, Y. Qiao, and C. Dong, “Unifying image processing as visual prompting question answering,” in Proceedings of International Conference on Machine Learning , 2024, pp. 30 873–30 891
2024
Closest in time.
D. Zheng, X.-M. Wu, S. Yang, J. Zhang, J.-F. Hu, and W.-S. Zheng, “Selective hourglass mapping for universal image restoration based on diffusion model,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 25 445–25 455
2024
Closest in time.
Z. Luo, F. K. Gustafsson, Z. Zhao, J. Sjölund, and T. B. Schön, “Controlling vision-language models for multi-task image restoration,” in Proceedings of the International Conference on Learning Representations , 2024, pp. 1–21
2024
Closest in time.
Y. Liu, Z. Ke, F. Liu, N. Zhao, and R. W. Lau, “Diff-plugin: Revitalizing details for diffusion-based low-level tasks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 4197–4208
2024
Closest in time.
S. Sun, W. Ren, X. Gao, R. Wang, and X. Cao, “Restoring images in adverse weather conditions via histogram transformer,” in Proceedings of the European Conference on Computer Vision , 2024, pp. 111–129
2024
Closest in time.
H. Jiang, A. Luo, X. Liu, S. Han, and S. Liu, “Lightendiffusion: Unsupervised low-light image enhancement with latent-retinex diffusion models,” in Proceedings of the European Conference on Computer Vision , 2024, pp. 161–179
2024
Closest in time.
2024
Closest in time.
Z. Chen, Z. He, and Z.-M. Lu, “Dea-net: Single image dehazing based on detail-enhanced convolution and content-guided attention,” IEEE Transactions on Image Processing , vol. 33, pp. 1002–1015, 2024
2024
Closest in time.
K. Jiang, J. Jiang, X. Liu, X. Xu, and X. Ma, “Fmrnet: Image deraining via frequency mutual revision,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 11, 2024, pp. 12 892–12 900
2024
Closest in time.
H. Li, R. Qiao, P. Yu, H. Li, and M. Tan, “Cthd-net: Cnn-transformer hybrid dehazing network via residual global attention and gated boosting strategy,” Journal of Visual Communication and Image Representation , vol. 99, p. 104066, 2024
2024
Closest in time.
C. Wang, L. Guo, Z. Fu, S. Yang, H. Cheng, A. C. Kot, and B. Wen, “Reconciling stochastic and deterministic strategies for zero-shot image restoration using diffusion model in dual,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2025
2025
Closest in time.
S. Rajagopalan and V. M. Patel, “Awracle: All-weather image restoration using visual in-context learning,” Proceedings of the AAAI Conference on Artificial Intelligence , 2025
2025
Closest in time.
J. Hu, L. Jin, Z. Yao, and Y. Lu, “Universal image restoration pre-training via degradation classification,” in Proceedings of the International Conference on Learning Representations , 2025
2025
Closest in time.
Y. Yang, C. Wang, R. Liu, L. Zhang, X. Guo, and D. Tao, “Self-augmented unpaired image dehazing via density and depth decomposition,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 2037–2046
2046
Closest in time.