VAEWGAN-NCO in image deblurring framework using variational autoencoders and Wasserstein generative adversarial network
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11760-024-03085-5.pdf
Reference22 articles.
1. Li, D., Zhang, Y., Cheung, K.C., Wang, X., Qin, H., Li, H.: Learning degradation representations for image deblurring. In: European Conference on Computer Vision, pp. 736–753. Springer, Cham (2022)
2. Kim, K., Lee, S., Cho, S.: Mssnet: multi-scale-stage network for single image deblurring. In: European Conference on Computer Vision, pp. 524–539. Springer, Cham (2022)
3. Duan, Z., Yu, H., Zhang, Q., Tian, L.: Parameter extraction of solar photovoltaic model based on nutcracker optimization algorithm. Appl. Sci. 13(11), 6710 (2023)
4. Vitoria, P., Georgoulis, S., Tulyakov, S., Bochicchio, A., Erbach, J., Li, Y.: Event-based image deblurring with dynamic motion awareness. In: European Conference on Computer Vision, pp. 95–112. Springer, Cham (2022)
5. Senthilkumar, G., Tamilarasi, K., Periasamy, J.K.: Cloud intrusion detection framework using variational auto encoder Wasserstein generative adversarial network optimized with archerfish hunting optimization algorithm. Wirel. Netw. 1–18 (2023)
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