Fetching the paper…
Reading the bibliography…
The large language model and high-level vision model have achieved impressive performance improvements with large datasets and model sizes.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al., 2020a · 1901
Earlier work this paper cites.
Single image haze removal using dark channel prior
He, K., 2009 · 2009
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A., 2010 · 2010
Earlier work this paper cites.
Nighttime haze removal based on a new imaging model, in: 2014 IEEE International Conference on Image Processing (ICIP)
Jing, Z., Yang, C., Wang, Z., 2014 · 2014
Earlier work this paper cites.
Investigating haze-relevant features in a learning framework for image dehazing, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Tang, K., Yang, J., Wang, J., 2014 · 2014
Earlier work this paper cites.
Ronneberger, O., P.Fischer, Brox, T., 2015 · 2015
Earlier work this paper cites.
Nighttime haze removal with glow and multiple light colors, in: IEEE International Conference on Computer Vision
Yu, L., Tan, R.T., Brown, M.S., 2015 · 2015
Earlier work this paper cites.
Night-time dehazing by fusion, in: IEEE International Conference on Image Processing
Ancuti, C., Ancuti, C.O., Vleeschouwer, C.D., Bovik, A., 2016 · 2016
Earlier work this paper cites.
Dehazenet: An end-to-end system for single image haze removal
Cai, B., Xu, X., Jia, K., Qing, C., Tao, D., 2016 · 2016
Earlier work this paper cites.
The cityscapes dataset for semantic urban scene understanding, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3213–3223
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B., 2016 · 2016
Earlier work this paper cites.
Fast haze removal for nighttime image using maximum reflectance prior, in: IEEE Conference on Computer Vision Pattern Recognition
Jing, Z., Yang, C., Shuai, F., Yu, K., Chang, W.C., 2017 · 2017
Earlier work this paper cites.
Aod-net: All-in-one dehazing network, in: Proceedings of the IEEE international conference on computer vision, pp. 4770–4778
Li, B., Peng, X., Wang, Z., Xu, J., Feng, D., 2017 · 2017
Cited alongside, same era.
Scene parsing through ade20k dataset, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 633–641
Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., Torralba, A., 2017 · 2017
Cited alongside, same era.
O-haze: A dehazing benchmark with real hazy and haze-free outdoor images, in: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Ancuti, C.O., Ancuti, C., Timofte, R., Vleeschouwer, C.D., 2018b · 2018
Cited alongside, same era.
Semantic foggy scene understanding with synthetic data
Sakaridis, Christos, Dai, Dengxin, Gool, V., Luc, 2018 · 2018
Cited alongside, same era.
Dense haze: A benchmark for image dehazing with dense-haze and haze-free images
Ancuti, C.O., Ancuti, C., Sbert, M., Timofte, R., 2019 · 2019
Learning transferable visual models from natural language supervision, in: International conference on machine learning, PMLR. pp. 8748–8763
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al., 2021 · 2021
Later among the works it cites.
Zero-shot text-to-image generation, in: International Conference on Machine Learning, PMLR. pp. 8821–8831
Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., Sutskever, I., 2021 · 2021
Later among the works it cites.
Learning to restore hazy video: A new real-world dataset and a new method, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9239–9248
Zhang, X., Dong, H., Pan, J., Zhu, C., Tai, Y., Wang, C., Li, J., Huang, F., Wang, F., 2021 · 2021
Later among the works it cites.
Fifo: Learning fog-invariant features for foggy scene segmentation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 18911–18921
Lee, S., Son, T., Kwak, S., 2022 · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Gated context aggregation network for image dehazing and deraining, in: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV)
Chen, D., He, M., Fan, Q., Liao, J., Hua, G., 2019 · 2019
Cited alongside, same era.
Semantic understanding of foggy scenes with purely synthetic data
Hahner, M., Dai, D., Sakaridis, C., Zaech, J.N., Van Gool, L., 2019 · 2019
Cited alongside, same era.
Nh-haze: An image dehazing benchmark with non-homogeneous hazy and haze-free images, in: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Ancuti, C.O., Ancuti, C., Timofte, R., 2020 · 2020
Cited alongside, same era.
Single image dehazing via multi-scale convolutional neural networks with holistic edges
Ren, W., Pan, J., Zhang, H., Cao, X., Yang, M.H., 2020 · 2020
Cited alongside, same era.
Nighttime dehazing with a synthetic benchmark, in: Proceedings of the 28th ACM International Conference on Multimedia, Association for Computing Machinery, New York, NY, USA. p. 2355–2363
Zhang, J., Cao, Y., Zha, Z.J., Tao, D., 2020 · 2020
Cited alongside, same era.
Scaling up visual and vision-language representation learning with noisy text supervision, in: International Conference on Machine Learning, PMLR. pp. 4904–4916
Jia, C., Yang, Y., Xia, Y., Chen, Y.T., Parekh, Z., Pham, H., Le, Q., Sung, Y.H., Li, Z., Duerig, T., 2021 · 2021
Cited alongside, same era.
I-haze: a dehazing benchmark with real hazy and haze-free indoor images
Ancuti, C.O., Ancuti, C., Timofte, R., Vleeschouwer, C.D., 2018a
Cited in the paper.
Vision transformers for single image dehazing
Song, Y., He, Z., Qian, H., Du, X., 2022 · 2022
Later among the works it cites.
Uformer: A general u-shaped transformer for image restoration, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 17683–17693
Wang, Z., Cun, X., Bao, J., Zhou, W., Liu, J., Li, H., 2022 · 2022
Later among the works it cites.
Two-step image dehazing with intra-domain and inter-domain adaptation
Yi, X., Ma, B., Zhang, Y., Liu, L., Wu, J., 2022 · 2022
Later among the works it cites.
Restormer: Efficient transformer for high-resolution image restoration, in: CVPR
Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.H., 2022 · 2022
Later among the works it cites.
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.Y., et al., 2023 · 2023
Closest in time.
Curricular contrastive regularization for physics-aware single image dehazing, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5785–5794
Zheng, Y., Zhan, J., He, S., Dong, J., Du, Y., 2023 · 2023
Closest in time.