{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T13:34:26Z","timestamp":1780320866370,"version":"3.54.1"},"reference-count":76,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Vis"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s11263-024-02161-0","type":"journal-article","created":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T15:02:38Z","timestamp":1722524558000},"page":"475-488","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Triplane-Smoothed Video Dehazing with CLIP-Enhanced Generalization"],"prefix":"10.1007","volume":"133","author":[{"given":"Jingjing","family":"Ren","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoyu","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tian","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongtao","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3871-663X","authenticated-orcid":false,"given":"Lei","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,8,1]]},"reference":[{"key":"2161_CR1","doi-asserted-by":"crossref","unstructured":"Abdelfattah, R., Guo, Q., Li, X., et al. (2023). Cdul: Clip-driven unsupervised learning for multi-label image classification. In Proceedings of the IEEE\/CVF international conference on computer vision (pp. 1348\u20131357).","DOI":"10.1109\/ICCV51070.2023.00130"},{"key":"2161_CR2","doi-asserted-by":"crossref","unstructured":"Barron, J.T., Mildenhall, B., Tancik, M., et al. (2021). Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. In Proceedings of the IEEE\/CVF international conference on computer vision (pp. 5855\u20135864).","DOI":"10.1109\/ICCV48922.2021.00580"},{"key":"2161_CR3","doi-asserted-by":"crossref","unstructured":"Barron, J. T., Mildenhall, B., Verbin, D., et\u00a0al. (2022). Mip-nerf 360: Unbounded anti-aliased neural radiance fields. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 5470\u20135479).","DOI":"10.1109\/CVPR52688.2022.00539"},{"key":"2161_CR4","doi-asserted-by":"crossref","unstructured":"Cao, A., & Johnson, J. (2023). Hexplane: A fast representation for dynamic scenes. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 130\u2013141).","DOI":"10.1109\/CVPR52729.2023.00021"},{"key":"2161_CR5","doi-asserted-by":"crossref","unstructured":"Chan, E. R., Lin, C. Z., Chan, M. A., et\u00a0al. (2022a). Efficient geometry-aware 3d generative adversarial networks. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 16123\u201316133).","DOI":"10.1109\/CVPR52688.2022.01565"},{"key":"2161_CR6","doi-asserted-by":"crossref","unstructured":"Chan, K. C., Zhou, S., Xu, X., et\u00a0al. (2022b). Basicvsr++: Improving video super-resolution with enhanced propagation and alignment. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 5972\u20135981).","DOI":"10.1109\/CVPR52688.2022.00588"},{"key":"2161_CR7","doi-asserted-by":"crossref","unstructured":"Chang, Y. L., Liu, Z. Y., Lee, K. Y., et\u00a0al. (2019). Free-form video inpainting with 3d gated convolution and temporal patchgan. In Proceedings of the IEEE\/CVF international conference on computer vision (pp. 9066\u20139075).","DOI":"10.1109\/ICCV.2019.00916"},{"key":"2161_CR8","doi-asserted-by":"crossref","unstructured":"Chen, H., Gu, J., Liu, Y., et\u00a0al. (2023a). Masked image training for generalizable deep image denoising. In Proceedings of the IEEE\/CVF Conference on computer vision and pattern recognition (pp. 1692\u20131703).","DOI":"10.1109\/CVPR52729.2023.00169"},{"key":"2161_CR9","doi-asserted-by":"crossref","unstructured":"Chen, H., Ren, J., Gu, J., et\u00a0al. (2023b). Snow removal in video: A new dataset and a novel method. In Proceedings of the IEEE\/CVF International conference on computer vision (pp. 13211\u201313222).","DOI":"10.1109\/ICCV51070.2023.01215"},{"key":"2161_CR10","unstructured":"Chen, J., Ren, X., Guo, Q., et\u00a0al. (2023c). Lrr: Language-driven resamplable continuous representation against adversarial tracking attacks. In International conference on machine learning."},{"key":"2161_CR11","doi-asserted-by":"crossref","unstructured":"Chen, S., Ye, T., Liu, Y., et\u00a0al. (2023d). Cplformer: Cross-scale prototype learning transformer for image snow removal. In Proceedings of the 31st ACM international conference on multimedia (pp. 4228\u20134239).","DOI":"10.1145\/3581783.3611893"},{"key":"2161_CR12","doi-asserted-by":"crossref","unstructured":"Chen, S., Ye, T., Xue, C., et\u00a0al. (2023e). Uncertainty-driven dynamic degradation perceiving and background modeling for efficient single image desnowing. In Proceedings of the 31st ACM international conference on multimedia (pp. 4269\u20134280).","DOI":"10.1145\/3581783.3612003"},{"key":"2161_CR13","doi-asserted-by":"crossref","unstructured":"Deng, Z., Zhu, L., Hu, X., et\u00a0al. (2019). Deep multi-model fusion for single-image dehazing. In Proceedings of the IEEE\/CVF international conference on computer vision (pp. 2453\u20132462).","DOI":"10.1109\/ICCV.2019.00254"},{"key":"2161_CR14","doi-asserted-by":"crossref","unstructured":"Dong, H., Pan, J., Xiang, L., et\u00a0al. (2020). Multi-scale boosted dehazing network with dense feature fusion. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 2157\u20132167).","DOI":"10.1109\/CVPR42600.2020.00223"},{"issue":"12","key":"2161_CR15","doi-asserted-by":"publisher","first-page":"2959","DOI":"10.1109\/26.477498","volume":"43","author":"AM Eskicioglu","year":"1995","unstructured":"Eskicioglu, A. M., & Fisher, P. S. (1995). Image quality measures and their performance. IEEE Transactions on Communications, 43(12), 2959\u20132965.","journal-title":"IEEE Transactions on Communications"},{"key":"2161_CR16","doi-asserted-by":"crossref","unstructured":"Esmaeilpour, S., Liu, B., Robertson, E., et\u00a0al. (2022). Zero-shot out-of-distribution detection based on the pre-trained model clip. In Proceedings of the AAAI conference on artificial intelligence (pp. 6568\u20136576).","DOI":"10.1609\/aaai.v36i6.20610"},{"key":"2161_CR17","doi-asserted-by":"crossref","unstructured":"Fridovich-Keil, S., Meanti, G., Warburg, F. R., et\u00a0al. (2023). K-planes: Explicit radiance fields in space, time, and appearance. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 12479\u201312488).","DOI":"10.1109\/CVPR52729.2023.01201"},{"key":"2161_CR18","doi-asserted-by":"crossref","unstructured":"Goyal, S., Kumar, A., Garg, S., et\u00a0al. (2023). Finetune like you pretrain: Improved finetuning of zero-shot vision models. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 19338\u201319347).","DOI":"10.1109\/CVPR52729.2023.01853"},{"key":"2161_CR19","doi-asserted-by":"crossref","unstructured":"Guizilini, V., Ambrus, R., Pillai, S., et\u00a0al. (2020). 3d packing for self-supervised monocular depth estimation. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 2485\u20132494).","DOI":"10.1109\/CVPR42600.2020.00256"},{"key":"2161_CR20","doi-asserted-by":"crossref","unstructured":"Guo, C.L., Yan, Q., Anwar, S., et\u00a0al. (2022). Image dehazing transformer with transmission-aware 3d position embedding. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 5812\u20135820).","DOI":"10.1109\/CVPR52688.2022.00572"},{"issue":"12","key":"2161_CR21","first-page":"2341","volume":"33","author":"K He","year":"2010","unstructured":"He, K., Sun, J., & Tang, X. (2010). Single image haze removal using dark channel prior. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(12), 2341\u20132353.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2161_CR22","doi-asserted-by":"crossref","unstructured":"Huang, C., Li, J., Li, B., et\u00a0al. (2022). Neural compression-based feature learning for video restoration. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 5872\u20135881).","DOI":"10.1109\/CVPR52688.2022.00578"},{"key":"2161_CR23","doi-asserted-by":"crossref","unstructured":"Jin, X., Han, L.H., Li, Z., et\u00a0al. (2023). Dnf: Decouple and feedback network for seeing in the dark. In IEEE conference on computer vision and pattern recognition (CVPR) (pp. 18135\u201318144).","DOI":"10.1109\/CVPR52729.2023.01739"},{"key":"2161_CR24","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., & Aila, T. (2019). A style-based generator architecture for generative adversarial networks. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp 4401\u20134410).","DOI":"10.1109\/CVPR.2019.00453"},{"key":"2161_CR25","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aittala, M., et\u00a0al. (2020). Analyzing and improving the image quality of stylegan. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 8110\u20138119).","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"2161_CR26","doi-asserted-by":"crossref","unstructured":"Kim, D., Woo, S., Lee, J. Y., et\u00a0al. (2020). Video panoptic segmentation. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 9859\u20139868).","DOI":"10.1109\/CVPR42600.2020.00988"},{"issue":"3","key":"2161_CR27","doi-asserted-by":"publisher","first-page":"410","DOI":"10.1016\/j.jvcir.2013.02.004","volume":"24","author":"JH Kim","year":"2013","unstructured":"Kim, J. H., Jang, W. D., Sim, J. Y., et al. (2013). Optimized contrast enhancement for real-time image and video dehazing. Journal of Visual Communication and Image Representation, 24(3), 410\u2013425.","journal-title":"Journal of Visual Communication and Image Representation"},{"key":"2161_CR28","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Mintun, E., Ravi, N., et\u00a0al. (2023). Segment anything. arXiv preprint arXiv:2304.02643.","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"2161_CR29","doi-asserted-by":"crossref","unstructured":"Li, B., Peng, X., Wang, Z., et\u00a0al. (2017). Aod-net: All-in-one dehazing network. In Proceedings of the IEEE international conference on computer vision (pp. 4770\u20134778).","DOI":"10.1109\/ICCV.2017.511"},{"key":"2161_CR30","doi-asserted-by":"crossref","unstructured":"Li, B., Peng, X., Wang, Z., et\u00a0al. (2018). End-to-end united video dehazing and detection. In Proceedings of the AAAI conference on artificial intelligence.","DOI":"10.1609\/aaai.v32i1.12287"},{"key":"2161_CR31","doi-asserted-by":"publisher","first-page":"8457","DOI":"10.1109\/TIP.2020.3016134","volume":"29","author":"B Li","year":"2020","unstructured":"Li, B., Gou, Y., Liu, J. Z., et al. (2020). Zero-shot image dehazing. IEEE Transactions on Image Processing, 29, 8457\u20138466.","journal-title":"IEEE Transactions on Image Processing"},{"key":"2161_CR32","doi-asserted-by":"publisher","first-page":"1754","DOI":"10.1007\/s11263-021-01431-5","volume":"129","author":"B Li","year":"2021","unstructured":"Li, B., Gou, Y., Gu, S., et al. (2021). You only look yourself: Unsupervised and untrained single image dehazing neural network. International Journal of Computer Vision, 129, 1754\u20131767.","journal-title":"International Journal of Computer Vision"},{"key":"2161_CR33","doi-asserted-by":"crossref","unstructured":"Li, B., Liu, X., Hu, P., et\u00a0al. (2022a). All-in-one image restoration for unknown corruption. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 17452\u201317462).","DOI":"10.1109\/CVPR52688.2022.01693"},{"issue":"12","key":"2161_CR34","doi-asserted-by":"publisher","first-page":"5664","DOI":"10.1109\/TIP.2016.2612882","volume":"25","author":"C Li","year":"2016","unstructured":"Li, C., Guo, J., Cong, R., et al. (2016). Underwater image enhancement by dehazing with minimum information loss and histogram distribution prior. IEEE Transactions on Image Processing, 25(12), 5664\u20135677.","journal-title":"IEEE Transactions on Image Processing"},{"issue":"3","key":"2161_CR35","doi-asserted-by":"publisher","first-page":"704","DOI":"10.1109\/TMM.2019.2933334","volume":"22","author":"C Li","year":"2019","unstructured":"Li, C., Guo, C., Guo, J., et al. (2019). Pdr-net: Perception-inspired single image dehazing network with refinement. IEEE Transactions on Multimedia, 22(3), 704\u2013716.","journal-title":"IEEE Transactions on Multimedia"},{"key":"2161_CR36","doi-asserted-by":"crossref","unstructured":"Li, C., Guo, C., Zhou, S., et\u00a0al. (2023). Flexicurve: Flexible piecewise curves estimation for photo retouching. In IEEE conference on computer vision and pattern recognition NTIRE workshop (CVPRW)-Oral (pp. 1092\u20131101).","DOI":"10.1109\/CVPRW59228.2023.00116"},{"key":"2161_CR37","unstructured":"Li, J., Li, D., Xiong, C., et\u00a0al. (2022b). Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In International conference on machine learning, PMLR (pp. 12888\u201312900)."},{"key":"2161_CR38","doi-asserted-by":"crossref","unstructured":"Liu, X., Ma, Y., Shi, Z., et\u00a0al. (2019). Griddehazenet: Attention-based multi-scale network for image dehazing. In Proceedings of the IEEE\/CVF international conference on computer vision (pp. 7314\u20137323).","DOI":"10.1109\/ICCV.2019.00741"},{"key":"2161_CR39","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wan, L., Fu, H., et\u00a0al. (2022a). Phase-based memory network for video dehazing. In Proceedings of the 30th ACM international conference on multimedia (pp. 5427\u20135435).","DOI":"10.1145\/3503161.3547998"},{"key":"2161_CR40","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C. Y., et\u00a0al. (2022b). A convnet for the 2020s. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 11976\u201311986).","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"2161_CR41","unstructured":"Luo, Z., Gustafsson, F. K., Zhao, Z., et\u00a0al. (2023). Controlling vision-language models for universal image restoration. arXiv preprint arXiv:2310.01018."},{"key":"2161_CR42","doi-asserted-by":"crossref","unstructured":"Mei, Y., Zhang, H., Zhang, X., et\u00a0al. (2023). Lightpainter: Interactive portrait relighting with freehand scribble. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 195\u2013205).","DOI":"10.1109\/CVPR52729.2023.00027"},{"issue":"1","key":"2161_CR43","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1145\/3503250","volume":"65","author":"B Mildenhall","year":"2021","unstructured":"Mildenhall, B., Srinivasan, P. P., Tancik, M., et al. (2021). Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1), 99\u2013106.","journal-title":"Communications of the ACM"},{"key":"2161_CR44","doi-asserted-by":"crossref","unstructured":"Nayar, S. K., & Narasimhan, S. G. (1999). Vision in bad weather. In Proceedings of the seventh IEEE international conference on computer vision, IEEE (pp0 820\u2013827).","DOI":"10.1109\/ICCV.1999.790306"},{"key":"2161_CR45","doi-asserted-by":"crossref","unstructured":"Oh, S. W., Lee, J. Y., Xu, N., et\u00a0al. (2019). Video object segmentation using space-time memory networks. In Proceedings of the IEEE\/CVF international conference on computer vision (pp. 9226\u20139235).","DOI":"10.1109\/ICCV.2019.00932"},{"key":"2161_CR46","doi-asserted-by":"crossref","unstructured":"Patashnik, O., Wu, Z., Shechtman, E., et\u00a0al. (2021). Styleclip: Text-driven manipulation of stylegan imagery. In Proceedings of the IEEE\/CVF international conference on computer vision (pp. 2085\u20132094).","DOI":"10.1109\/ICCV48922.2021.00209"},{"key":"2161_CR47","doi-asserted-by":"crossref","unstructured":"Qin, X., Wang, Z., Bai, Y., et\u00a0al. (2020). Ffa-net: Feature fusion attention network for single image dehazing. In Proceedings of the AAAI conference on artificial intelligence (pp. 11908\u201311915).","DOI":"10.1609\/aaai.v34i07.6865"},{"key":"2161_CR48","unstructured":"Radford, A., Kim, J.W., Hallacy, C., et\u00a0al. (2021). Learning transferable visual models from natural language supervision. In International conference on machine learning, PMLR (pp. 8748\u20138763)."},{"key":"2161_CR49","doi-asserted-by":"crossref","unstructured":"Ren, S., Liu, W., Liu, Y., et\u00a0al. (2021). Reciprocal transformations for unsupervised video object segmentation. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 15455\u201315464).","DOI":"10.1109\/CVPR46437.2021.01520"},{"key":"2161_CR50","doi-asserted-by":"crossref","unstructured":"Ren, S., Gao, Z., Hua, T., et\u00a0al. (2022a). Co-advise: Cross inductive bias distillation. In Proceedings of the IEEE\/CVF Conference on computer vision and pattern recognition (pp. 16773\u201316782).","DOI":"10.1109\/CVPR52688.2022.01627"},{"key":"2161_CR51","doi-asserted-by":"crossref","unstructured":"Ren, S., Wang, H., Gao, Z., et\u00a0al. (2022b). A simple data mixing prior for improving self-supervised learning. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 14595\u201314604).","DOI":"10.1109\/CVPR52688.2022.01419"},{"key":"2161_CR52","doi-asserted-by":"crossref","unstructured":"Ren, S., Wei, F., Zhang, Z., et\u00a0al. (2023). Tinymim: An empirical study of distilling mim pre-trained models. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 3687\u20133697).","DOI":"10.1109\/CVPR52729.2023.00359"},{"issue":"4","key":"2161_CR53","doi-asserted-by":"publisher","first-page":"1895","DOI":"10.1109\/TIP.2018.2876178","volume":"28","author":"W Ren","year":"2018","unstructured":"Ren, W., Zhang, J., Xu, X., et al. (2018). Deep video dehazing with semantic segmentation. IEEE Transactions on Image Processing, 28(4), 1895\u20131908.","journal-title":"IEEE Transactions on Image Processing"},{"key":"2161_CR54","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., et\u00a0al. (2022). High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 10684\u201310695).","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"2161_CR55","doi-asserted-by":"crossref","unstructured":"Ruiz, N., Li, Y., Jampani, V., et\u00a0al. (2023). Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 22500\u201322510).","DOI":"10.1109\/CVPR52729.2023.02155"},{"key":"2161_CR56","doi-asserted-by":"crossref","unstructured":"Sain, A., Bhunia, A. K., Chowdhury, P. N., et\u00a0al. (2023). Clip for all things zero-shot sketch-based image retrieval, fine-grained or not. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 2765\u20132775).","DOI":"10.1109\/CVPR52729.2023.00271"},{"key":"2161_CR57","doi-asserted-by":"crossref","unstructured":"Silberman, N., Hoiem, D., Kohli, P., et\u00a0al. (2012). Indoor segmentation and support inference from rgbd images. In Computer Vision\u2013ECCV 2012: 12th European conference on computer vision, Florence, Italy, October 7\u201313, 2012, proceedings, part, (Vol. 12, pp 746\u2013760). Springer.","DOI":"10.1007\/978-3-642-33715-4_54"},{"key":"2161_CR58","doi-asserted-by":"publisher","first-page":"1927","DOI":"10.1109\/TIP.2023.3256763","volume":"32","author":"Y Song","year":"2023","unstructured":"Song, Y., He, Z., Qian, H., et al. (2023). Vision transformers for single image dehazing. IEEE Transactions on Image Processing, 32, 1927\u20131941.","journal-title":"IEEE Transactions on Image Processing"},{"key":"2161_CR59","doi-asserted-by":"crossref","unstructured":"Wang, J., Chan, K.C., & Loy, C.C. (2023). Exploring clip for assessing the look and feel of images. In Proceedings of the AAAI conference on artificial intelligence (pp. 2555\u20132563).","DOI":"10.1609\/aaai.v37i2.25353"},{"key":"2161_CR60","doi-asserted-by":"crossref","unstructured":"Wang, X., Chan, K. ., Yu, K., et\u00a0al. (2019). Edvr: Video restoration with enhanced deformable convolutional networks. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops.","DOI":"10.1109\/CVPRW.2019.00247"},{"issue":"4","key":"2161_CR61","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A. C., Sheikh, H. R., et al. (2004). Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600\u2013612.","journal-title":"IEEE Transactions on Image Processing"},{"key":"2161_CR62","doi-asserted-by":"crossref","unstructured":"Wu, H., Qu, Y., Lin, S., et\u00a0al. (2021). Contrastive learning for compact single image dehazing. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 10551\u201310560).","DOI":"10.1109\/CVPR46437.2021.01041"},{"key":"2161_CR63","doi-asserted-by":"crossref","unstructured":"Wu, R. Q., Duan, Z. P., Guo, C. L., et\u00a0al. (2023). Ridcp: Revitalizing real image dehazing via high-quality codebook priors. In IEEE conference on computer vision and pattern recognition (CVPR).","DOI":"10.1109\/CVPR52729.2023.02134"},{"key":"2161_CR64","doi-asserted-by":"crossref","unstructured":"Xia, W., Yang, Y., Xue, J. H., et\u00a0al. (2021). Tedigan: Text-guided diverse face image generation and manipulation. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 2256\u20132265).","DOI":"10.1109\/CVPR46437.2021.00229"},{"key":"2161_CR65","doi-asserted-by":"crossref","unstructured":"Xu, J., Hu, X., Zhu, L., et\u00a0al.: (2023) Video dehazing via a multi-range temporal alignment network with physical prior. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 18053\u201318062).","DOI":"10.1109\/CVPR52729.2023.01731"},{"key":"2161_CR66","unstructured":"Yu, F., & Koltun, V. (2015). Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122"},{"key":"2161_CR67","doi-asserted-by":"crossref","unstructured":"Yu, H., Zheng, N., Zhou, M., et\u00a0al. (2022). Frequency and spatial dual guidance for image dehazing. In European conference on computer vision (pp. 181\u2013198). Springer.","DOI":"10.1007\/978-3-031-19800-7_11"},{"key":"2161_CR68","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1007\/s00371-011-0569-8","volume":"27","author":"J Zhang","year":"2011","unstructured":"Zhang, J., Li, L., Zhang, Y., et al. (2011). Video dehazing with spatial and temporal coherence. The Visual Computer, 27, 749\u2013757.","journal-title":"The Visual Computer"},{"key":"2161_CR69","unstructured":"Zhang, R., Gu, J., Chen, H., et\u00a0al. (2023) Crafting training degradation distribution for the accuracy-generalization trade-off in real-world super-resolution. In International conference on machine learning, PMLR (pp. 41078\u201341091)."},{"key":"2161_CR70","doi-asserted-by":"crossref","unstructured":"Zhang, X., Dong, H., Pan, J., et\u00a0al. (2021). 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\u20139248.)","DOI":"10.1109\/CVPR46437.2021.00912"},{"key":"2161_CR71","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., et\u00a0al. (2017). Pyramid scene parsing network. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2881\u20132890).","DOI":"10.1109\/CVPR.2017.660"},{"key":"2161_CR72","unstructured":"Zhao, S., Chen, D., Chen, Y. C., et\u00a0al.: (2023). Uni-controlnet: All-in-one control to text-to-image diffusion models. arXiv preprint arXiv:2305.16322"},{"key":"2161_CR73","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Zhan, J., He, S., et\u00a0al. (2023). Curricular contrastive regularization for physics-aware single image dehazing. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 5785\u20135794).","DOI":"10.1109\/CVPR52729.2023.00560"},{"key":"2161_CR74","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Ren, W., Cao, X., et\u00a0al. (2021). Ultra-high-definition image dehazing via multi-guided bilateral learning. In 2021 IEEE\/CVF conference on computer vision and pattern recognition (CVPR) (pp. 16180\u201316189). IEEE.","DOI":"10.1109\/CVPR46437.2021.01592"},{"key":"2161_CR75","unstructured":"Zhou, M., Huang, J., Guo, C. L., et\u00a0al. (2023a). Fourmer: An efficient global modeling paradigm for image restoration. In International conference on machine learning (ICML) (pp. 42589\u201342601). PMLR."},{"key":"2161_CR76","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Lei, Y., Zhang, B., et\u00a0al. (2023b). Zegclip: Towards adapting clip for zero-shot semantic segmentation. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 11175\u201311185).","DOI":"10.1109\/CVPR52729.2023.01075"}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-024-02161-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-024-02161-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-024-02161-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T06:17:02Z","timestamp":1736230622000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-024-02161-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,1]]},"references-count":76,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["2161"],"URL":"https:\/\/doi.org\/10.1007\/s11263-024-02161-0","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,1]]},"assertion":[{"value":"31 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 August 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}