Khung Hình Ảnh Khử Sương Mù Không Ghép Đôi Ổn Định Qua Phân Tích Độ Dày và Độ Sâu

Ancuti, C., Ancuti, C. O., Timofte, R., De Vleeschouwer, C. (2018) I-haze: A dehazing benchmark with real hazy and haze-free indoor images. In International conference on advanced concepts for intelligent vision systems (pp. 620–631). Springer. Berman, D., et al. (2016). Non-local image dehazing. In IEEE transactions on pattern analysis and machine intelligence (pp. 1674–1682). Cai, B., Xu, X., Jia, K., Qing, C., & Tao, D. (2016). Dehazenet: An end-to-end system for single image haze removal. IEEE Transactions on Image Processing, 25(11), 5187–5198. Chang, M., Li, Q., Feng, H., & Xu, Z.(2020). Spatial-adaptive network for single image denoising. In European conference on computer vision (pp. 171–187). Springer. Chen, L.C., & Zhu, Y., Papandreou, G., Schroff, F., Adam, H.(2018). Encoder–decoder with atrous separable convolution for semantic image segmentation. In European conference on computer vision (pp. 801–818). Chen, T., Kornblith, S., Norouzi, M., & Hinton, G.(2020). A simple framework for contrastive learning of visual representations. In International conference on machine learning (pp. 1597–1607). PMLR. Chen, Z., Wang, Y., Yang, Y., & Liu, D.(2021). Psd: Principled synthetic-to-real dehazing guided by physical priors. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7180–7189). Choi, L. K., You, J., & Bovik, A. C. (2015). Referenceless prediction of perceptual fog density and perceptual image defogging. IEEE Transactions on Image Processing, 24(11), 3888–3901. Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., et al. (2016). The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3213–3223). Deng, Q., Huang, Z., Tsai, C. C., & Lin, C. W. (2020). Hardgan: A haze-aware representation distillation GAN for single image dehazing. In European conference on computer vision (pp. 722–738). Springer. Dong, H., Pan, J., Xiang, L., Hu, Z., Zhang, X., Wang, F., et al. (2020). Multi-scale boosted dehazing network with dense feature fusion. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2157–2167). Dudhane, A., & Murala, S.(2019). Cdnet: Single image de-hazing using unpaired adversarial training. In WACV (pp. 1147–1155). Engin, D., Genç, A., & Kemal Ekenel, H.(2018). Cycle-dehaze: Enhanced cyclegan for single image dehazing. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops (pp. 825–833). Fattal, R. (2014). Dehazing using color-lines. ACM Transaction on Graphics, 34(1), 1–14. Geiger, A., Lenz, P., & Urtasun, R. (2012). Are we ready for autonomous driving? The kitti vision benchmark suite. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3354–3361). IEEE. Golts, A., Freedman, D., & Elad, M. (2019). Unsupervised single image dehazing using dark channel prior loss. IEEE Transactions on Image Processing, 29, 2692–2701. Guo, X., Yang, Y., Wang, C., & Ma, J. (2022). Image dehazing via enhancement, restoration, and fusion: A survey. Information Fusion, 86–87, 146–170. Han, J., Shoeiby, M., Malthus, T., Botha, E., Anstee, J., Anwar, S., et al. (2021). Single underwater image restoration by contrastive learning. In IEEE international geoscience and remote sensing symposium (IGARSS) (pp. 2385–2388). IEEE. He, F., Liu, T., & Tao, D. (2020). Why resnet works? Residuals generalize. IEEE Transactions on Neural Networks and Learning Systems, 31(12), 5349–5362. He, F., & Tao, D.(2020). Recent advances in deep learning theory. arXiv preprint arXiv:2012.10931 He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R.(2020). Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 9729–9738). 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), 2341–2353. He, K., Sun, J., & Tang, X. (2012). Guided image filtering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(6), 1397–1409. He, T., Zhang, Z., Zhang, H., Zhang, Z., Xie, J., & Li, M.(2019). Bag of tricks for image classification with convolutional neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 558–567). Hu, Q., Zhang, Y., Zhu, Y., Jiang, Y., & Song, M. (2023). Single image dehazing algorithm based on sky segmentation and optimal transmission maps. The Visual Computer, 39(3), 997–1013. Huang, S. C., Le, T. H., & Jaw, D. W. (2020). Dsnet: Joint semantic learning for object detection in inclement weather conditions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(8), 2623–2633. Isola, P., Zhu, J. Y., Zhou, T., & Efros, A.A.(2017). Image-to-image translation with conditional adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 5967–5976). Jin, Y., Gao, G., Liu, Q., & Wang, Y.(2020). Unsupervised conditional disentangle network for image dehazing. In IEEE international conference on image processing (pp. 963–967). IEEE. Kingma, D. P., & Ba, J.(2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 Laina, I., Rupprecht, C., Belagiannis, V., Tombari, F., & Navab, N.(2016). Deeper depth prediction with fully convolutional residual networks. In International conference on 3D vision (pp. 239–248). Li, B., Gou, Y., Gu, S., Liu, J. Z., Zhou, J. T., & Peng, X. (2021). You only look yourself: Unsupervised and untrained single image dehazing neural network. International Journal of Computer Vision, 129(5), 1754–1767. Li, B., Gou, Y., Liu, J. Z., Zhu, H., Zhou, J. T., & Peng, X. (2020). Zero-shot image dehazing. IEEE Transactions on Image Processing, 29, 8457–8466. Li, B., Peng, X., Wang, Z., Xu, J., & Feng, D.(2017). Aod-net: All-in-one dehazing network. In Proceedings of the IEEE international conference on computer vision (pp. 4770–4778). Li, B., Ren, W., Fu, D., Tao, D., Feng, D., Zeng, W., & Wang, Z. (2019). Benchmarking single-image dehazing and beyond. IEEE Transactions on Image Processing, 28(1), 492–505. Li, R., Pan, J., Li, Z., & Tang, J.(2018). Single image dehazing via conditional generative adversarial network. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 8202–8211). Li, X., Wu, J., Lin, Z., Liu, H., & Zha, H. (2018). Recurrent squeeze-and-excitation context aggregation net for single image deraining. In European conference on computer vision, pp. 254–269. Li, Z., Wang, C., Zheng, H., Zhang, J., & Li, B.(2022). Fakeclr: Exploring contrastive learning for solving latent discontinuity in data-efficient gans. arXiv preprint arXiv:2207.08630 Liu, W., Hou, X., Duan, J., & Qiu, G. (2020). End-to-end single image fog removal using enhanced cycle consistent adversarial networks. IEEE Transactions on Image Processing, 29, 7819–7833. Liu, X., Ma, Y., Shi, Z., & Chen, J.(2019) Griddehazenet: Attention-based multi-scale network for image dehazing. In Proceedings of the IEEE international conference on computer vision (pp. 7314–7323). Liu, Y., Li, H., & Wang, M. (2017). Single image dehazing via large sky region segmentation and multiscale opening dark channel model. IEEE Access, 5, 8890–8903. Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., & Paul Smolley, S.(2017). Least squares generative adversarial networks. In Proceedings of the IEEE international conference on computer vision (pp. 2794–2802). Narasimhan, S. G., & Nayar, S. K. (2000). Chromatic framework for vision in bad weather. In Proceedings of the IEEE conference on computer vision and pattern recognition (Vol. 1, pp. 598–605). Narasimhan, S. G., & Nayar, S. K. (2002). Vision and the atmosphere. International Journal of Computer Vision, 48(3), 233–254. Nathan Silberman Derek Hoiem, P. K., & Fergus, R. (2012). Indoor segmentation and support inference from RGBD images. In European conference on computer vision. Oord, A.v.d., Li, Y., & Vinyals, O. (2018). Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 Park, T., Efros, A. A., Zhang, R., & Zhu, J. Y. (2020). Contrastive learning for unpaired image-to-image translation. In European conference on computer vision (pp. 319–345). Springer. Qin, X., Wang, Z., Bai, Y., Xie, X., & Jia, H.(2020). Ffa-net: Feature fusion attention network for single image dehazing. In AAAI conference on artificial intelligence (Vol. 34, pp. 11908–11915). Qu, Y., Chen, Y., Huang, J., & Xie, Y. (2019). Enhanced pix2pix dehazing network. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 8160–8168). Ranftl, R., Lasinger, K., Hafner, D., Schindler, K., & Koltun, V. (2020). Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(3), 1623–1637. Redmon, J., & Farhadi, A. (2018) Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 Ren, D., Zuo, W., Hu, Q., Zhu, P., & Meng, D.(2019). Progressive image deraining networks: A better and simpler baseline. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3937–3946). Ren, W., Liu, S., Zhang, H., Pan, J., Cao, X., & Yang, M.H.(2016) Single image dehazing via multi-scale convolutional neural networks. In European conference on computer vision (pp. 154–169). Ronneberger, O., Fischer, P., & Brox, T.(2015). U-net: Convolutional networks for biomedical image segmentation. In International conference on medical image computing and computer assisted intervention (pp. 234–241). Sakaridis, C., Dai, D., Hecker, S., & Van Gool, L. (2018). Model adaptation with synthetic and real data for semantic dense foggy scene understanding. In ECCV (pp. 707–724). Sakaridis, C., Dai, D., & Van Gool, L. (2018). Semantic foggy scene understanding with synthetic data. International Journal of Computer Vision, 126(9), 973–992. Salazar-Colores, S., Moya-Sanchez, E. U., Ramos-Arreguin, J. M., Cabal-Yepez, E., Flores, G., & Cortes, U. (2020). Fast single image defogging with robust sky detection. IEEE Access, 8, 149176–149189. Shao, Y., Li, L., Ren, W., Gao, C., & Sang, N.(2020). Domain adaptation for image dehazing. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2808–2817). Sharma, G., Wu, W., & Dalal, E. N. (2005). The ciede2000 color-difference formula: Implementation notes, supplementary test data, and mathematical observations. Color Research & Application, 30(1), 21–30. Shen, Y., Deng, S., Yang, W., Wei, M., Xie, H., Zhang, X., Qin, J., & Wang, M. (2022). Semi-DRDNet semi-supervised detail-recovery image deraining network via unpaired contrastive learning. arXiv preprint arXiv:2204.02772 Shyam, P., Yoon, K.J., & Kim, K.S.(2021). Towards domain invariant single image dehazing. In AAAI conference on artificial intelligence (Vol. 35, pp. 9657–9665). Simonyan, K., & Zisserman, A. (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 Tan, M., & Le, Q.(2019). Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning (pp. 6105–6114). Wang, W., Yuan, X., Wu, X., & Liu, Y. (2017). Dehazing for images with large sky region. Neurocomputing, 238, 365–376. Wang, Y., Yan, X., Wang, F.L., Xie, H., Yang, W., Wei, M., & Qin, J. (2022). Ucl-dehaze: Towards real-world image dehazing via unsupervised contrastive learning. arXiv preprint arXiv:2205.01871 Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600–612. Wei, P., Wang, X., Wang, L., & Xiang, J.(2021). Sidgan: Single image dehazing without paired supervision. In International conference on pattern recognition (pp. 2958–2965). IEEE. Wu, H., Qu, Y., Lin, S., Zhou, J., Qiao, R., Zhang, Z., Xie, Y., & Ma, L.(2021). Contrastive learning for compact single image dehazing. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 10551–10560). Xian, K., Shen, C., Cao, Z., Lu, H., Xiao, Y., Li, R., & Luo, Z.(2018). Monocular relative depth perception with web stereo data supervision. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 311–320). Yang, F., Yang, H., Fu, J., Lu, H., & Guo, B.(2020). Learning texture transformer network for image super-resolution. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 5791–5800). Yang, X., Xu, Z., & Luo, J.(2018). Towards perceptual image dehazing by physics-based disentanglement and adversarial training. In AAAI conference on artificial intelligence (Vol. 32, pp. 7485–7492). Yang, Y., Wang, C., Liu, R., Zhang, L., Guo, X., & Tao, D.(2022). Self-augmented unpaired image dehazing via density and depth decomposition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2037–2046). Zhang, J., Lu, S., Zhan, F., & Yu, Y.(2021). Blind image super-resolution via contrastive representation learning. arXiv preprint arXiv:2107.00708 Zhang, K., Zuo, W., Chen, Y., Meng, D., & Zhang, L. (2017). Beyond a gaussian denoiser: Residual learning of deep CNN for image denoising. IEEE Transactions on Image Processing, 26(7), 3142–3155. Zhang, Y., Tian, Y., Kong, Y., Zhong, B., & Fu, Y. (2018). Residual dense network for image super-resolution. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2472–2481). Zhao, J., Zhang, J., Li, Z., Hwang, J. N., Gao, Y., Fang, Z., Jiang, X., & Huang, B. (2019). Dd-cyclegan: Unpaired image dehazing via double-discriminator cycle-consistent generative adversarial network. Engineering Applications of Artificial Intelligence, 82, 263–271. Zhao, S., Zhang, L., Shen, Y., & Zhou, Y. (2021). Refinednet: A weakly supervised refinement framework for single image dehazing. IEEE Transactions on Image Processing, 30, 3391–3404. Zhu, J. Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision (pp. 2223–2232). Zhu, Q., Mai, J., & Shao, L. (2015). A fast single image haze removal algorithm using color attenuation prior. IEEE Transactions on Image Processing, 24(11), 3522–3533.