Generative adversarial networks in computer vision: image synthesis and manipulation

[1] Krishna, A., Bartake, K., Niu, C., Wang, G., Lai, Y., & Jia, X., et al. (2021). Image synthesis for data augmentation in medical ct using deep reinforcement learning. arXiv e-prints. Search in Google Scholar

[2] Shin, M., Seo, Y., Bae, J., Choi, Y. S., Kim, H., & Byun, H., et al. (2023). Ballgan: 3d-aware image synthesis with a spherical background. arXiv e-prints. Search in Google Scholar

[3] Zhou, R., Jiang, C., & Xu, Q. (2021). A survey on generative adversarial network-based text-to-image synthesis. Neurocomputing, 451(1). Search in Google Scholar

[4] Wang, M., Lang, C., Feng, S., Wang, T., & Li, Y. (2021). Text to photo-realistic image synthesis via chained deep recurrent generative adversarial network. Journal of Visual Communication and Image Representation, 74, 102955. Search in Google Scholar

[5] Huang, S., & Chen, Y. (2022). Generative adversarial networks with adaptive semantic normalization for text-to-image synthesis. Digital Signal Processing(120-), 120. Search in Google Scholar

[6] Li, L., Sun, Y., Hu, F., Zhou, T., & Ren, J. (2020). Text to realistic image generation with attentional concatenation generative adversarial networks. Discrete Dynamics in Nature and Society, 2020, 1-10. Search in Google Scholar

[7] Tan, Y. X., Lee, C. P., Neo, M., Lim, K. M., & Lim, J. Y. (2022). Enhanced text-to-image synthesis conditional generative adversarial networks. IAENG Internaitonal journal of computer science(1 Pt.2), 49. Search in Google Scholar

[8] Zhang, H., Xu, T., Li, H., Zhang, S., Wang, X., & Huang, X., et al. (2017). Stackgan: text to photo-realistic image synthesis with stacked generative adversarial networks. IEEE. Search in Google Scholar

[9] Li, Z., Deng, C., Yang, E., & Tao, D. (2020). Staged sketch-to-image synthesis via semi-supervised generative adversarial networks. IEEE Transactions on Multimedia, PP(99), 1-1. Search in Google Scholar

[10] Tyagi, S., & Yadav, D. (2021). A comprehensive review on image synthesis with adversarial networks: theory, literature, and applications. Archives of Computational Methods in Engineering(4). Search in Google Scholar

[11] Costa, P., Galdran, A., Maria Inês Meyer, Michael David Abràmoff, & Aurélio Campilho. (2017). Towards adversarial retinal image synthesis. IEEE Transactions on Medical Imaging. Search in Google Scholar

[12] Qi, Z., Fan, C., Xu, L., Li, X., & Zhan, S. (2021). Mrp-gan: multi-resolution parallel generative adversarial networks for text-to-image synthesis. Pattern Recognition Letters, 147(11). Search in Google Scholar

[13] Dar, S. U., Yurt, M., Karacan, L., Erdem, A., Erdem, E., & Cukur, T. (2019). Image synthesis in multi-contrast mri with conditional generative adversarial networks. IEEE Transactions on Medical Imaging, 1-1. Search in Google Scholar

[14] Zhan, B., Li, Z., Pu, Y., Zhou, J., Wang, Y., & Zhou, L., et al. (2022). D2fe-gan: decoupled dual feature extraction based gan for mri image synthesis. Knowledge-based systems. Search in Google Scholar

[15] Li, F., Yu, Z., & Qin, C. (2022). Gan-based spatial image steganography with cross feedback mechanism. Signal Processing, 190, 108341-. Search in Google Scholar

[16] Tang, Z., Wang, J., Yuan, B., Li, H., Zhang, J., & Wang, H. (2022). Markov-gan: markov image enhancement method for malicious encrypted traffic classification. IET information security. Search in Google Scholar

[17] Emami, H., Aliabadi, M. M., Dong, M., & Chinnam, R. (2020). Spa-gan: spatial attention gan for image-to-image translation. IEEE Transactions on Multimedia, PP(99), 1-1. Search in Google Scholar

[18] Liu, K., Ye, Z., Guo, H., Cao, D., & Wang, F. Y. (2021). Fiss gan: a generative adversarial network for foggy image semantic segmentation. IEEE/CAA Journal of Automatica Sinica, 8(8), 1428-1439. Search in Google Scholar