Wang D, Wang Z. Research and implementation of image rain removal based on deep learning. International Journal of Advanced Network, Monitoring and Controls. 2022;7(3):25–32.
Cheng L, Liu N, Guo X, Shen Y, Meng Z, Huang K, Zhang X. A novel rain removal approach for outdoor dynamic vision sensor event videos. Front Neurorobot. 2022;16: 928707.
Liang X, Zhao F. Single-image rain removal network based on an attention mechanism and a residual structure. IEEE Access. 2022;10:52472–80.
Vu, D. T., Gonzalez, J. L., & Kim, M. (2021). Exploiting global and local attentions for heavy rain removal on single images. arXiv preprint arXiv:2104.08126.
Huang S, Xu Y, Ren M, Yang Y, Wan W. Rain removal of single image based on directional gradient priors. Appl Sci. 2022;12(22):11628.
He X, Feng Y, Xie Q, Wang J. Low-rank appearance-preserving rain streak removal from single images. Commun Inf Syst. 2022;22(1):79–102.
Jayaraman, T., &Chinnusamy, S. (2022). Performance analysis of a dual stage deep rain streak removal convolution neural network module with a modified deep residual dense network. International Journal of Applied Mathematics and Computer Science, 32(1).
Jiang N, Chen W, Lin L, Zhao T. Single image rain removal via multi-module deep grid network. Comput Vis Image Underst. 2021;202: 103106.
Wang H, Wu Y, Li M, Zhao Q, Meng D. Survey on rain removal from videos or a single image. SCIENCE CHINA Inf Sci. 2022;65(1): 111101.
Article MathSciNet Google Scholar
Swaminathan, R., &Korupolu, P. (2023). MobileDeRainGAN: an efficient semi-supervised approach to single image rain removal for task-driven applications. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 192–201).
Hettiarachchi P, Nawaratne R, Alahakoon D, De Silva D, Chilamkurti N. Rain streak removal for single images using conditional generative adversarial networks. Appl Sci. 2021;11(5):2214.
Fu, X., Qi, Q., Zha, Z. J., Zhu, Y., & Ding, X. (2021, May). Rain streak removal via dual graph convolutional network.In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 2, pp. 1352–1360).
Fan W, Wu Y, Wang C. Single image rain streak removal via layer similarity prior. Appl Intell. 2021;51:5822–35.
Su Z, Zhang Y, Zhang XP, Qi F. Non-local channel aggregation network for single image rain removal. Neurocomputing. 2022;469:261–72.
Daneshfar F, Bartani A, Lotfi P. Image captioning by diffusion models: a survey. Eng Appl Artif Intell. 2024;138: 109288.
He Y, Zeng T, Xiong Y, Li J, Wei H. Deep leaning based frequency-aware single image deraining by extracting knowledge from rain and background. Machine Learning and Knowledge Extraction. 2022;4(3):738–52.
Li M, Cao X, Zhao Q, Zhang L, Meng D. Online rain/snow removal from surveillance videos. IEEE Trans Image Process. 2021;30:2029–44.
Shen, Y., Wang, Y., Wei, M., Chen, H., Xie, H., Cheng, G., & Wang, F. L. (2022, October). Semi‐MoreGAN: semi‐supervised generative adversarial network for mixture of rain removal. In Computer Graphics Forum (Vol. 41, No. 7, pp. 443–454).
Mi Y, Yuan S, Li X, Zhou J. Dense residual generative adversarial network for rapid rain removal. IEEE Access. 2021;9:24848–58.
Pan, X., Yang, Y., Yang, C., Wang, C., & Tan, A. (2021, September). Two-stage fusion model for heavy rain removal on single image. In Journal of Physics: Conference Series (Vol. 2035, No. 1, p. 012041). IOP Publishing.
Yang Y, Xu M, Chen C, Xue F. Removing rain streaks from visual image using a combination of bilateral filter and generative adversarial network. Appl Sci. 2023;13(11):6387.
Arora, S., Bindra, S., Ahmad, M., & Ahmad, T. (2022, June). Decomposition makes better rain removal: an enhanced attention-guided image de-raining using deconvolutions network. In Proceedings of Second International Conference in Mechanical and Energy Technology: ICMET 2021, India (pp. 323–331). Singapore: Springer Nature Singapore.
Sivaanpu, A., &Thanikasalam, K. (2022, December). A dual CNN architecture for single image raindrop and rain streak removal.In 2022 7th International Conference on Information Technology Research (ICITR) (pp. 1–6).IEEE.
Frants, V., Agaian, S., & Panetta, K. (2023). QSAM-Net: rain streak removal by quaternion neural network with self-attention module. IEEE Transactions on Multimedia.
Yan, T., Li, M., Li, B., Yang, Y., & Lau, R. W. (2022). Rain removal from light field images with 4D convolution and multi-scale Gaussian process. arXiv preprint arXiv:2208.07735.
Zhou M, Zhao X, Luo F, Luo J, Pu H, Xiang T. Robust Rgb-T tracking via adaptive modality weight correlation filters and cross-modality learning. ACM Trans Multimed Comput Commun Appl. 2023;20(4):1–20.
Zhou M, Lan X, Wei X, Liao X, Mao Q, Li Y, Wu C, Xiang T, Fang B. An end-to-end blind image quality assessment method using a recurrent network and self-attention. IEEE Trans Broadcast. 2022;69(2):369–77.
Zhou M, Chen L, Wei X, Liao X, Mao Q, Wang H, Pu H, Luo J, Xiang T, Fang B. Perception-oriented U-shaped transformer network for 360-degree no-reference image quality assessment. IEEE Trans Broadcast. 2023;69(2):396–405.
Comments (0)