Acceso abierto

Research on the Improvement of Image Super Resolution Reconstruction Algorithm Based on AWSRN Model

, ,  y   
16 jun 2025

Cite
Descargar portada

Dong, C., Loy, C. C., & Tang, X, “Accelerating the Super-Resolution Convolutional Neural Network,” in Proc. European Conference on Computer Vision (ECCV), Springer, pp. 391–407, 2020. Dong C. Loy C. C. Tang X Accelerating the Super-Resolution Convolutional Neural Network ,” in Proc. European Conference on Computer Vision (ECCV) , Springer , pp. 391 407 , 2020 . Search in Google Scholar

J. Kim, J. K. Lee, and K. M. Lee, “Deep Recursive Residual Network for Image Super-Resolution,” in Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3566–3575, 2021. Kim J. Lee J. K. Lee K. M. Deep Recursive Residual Network for Image Super-Resolution ,” in Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 3566 3575 , 2021 . Search in Google Scholar

Y. Zhang, K. Li, K. Li, L. Wang, B. Zhong, and Y. Fu, “ Residual Recursive Network for Image Super-Resolution,” in Proc. AAAI Conference on Artificial Intelligence (AAAI), vol. 36, no. 3, pp. 3456–3464, 2022. Zhang Y. Li K. Li K. Wang L. Zhong B. Fu Y. Residual Recursive Network for Image Super-Resolution ,” in Proc. AAAI Conference on Artificial Intelligence (AAAI) , vol. 36 , no. 3 , pp. 3456 3464 , 2022 . Search in Google Scholar

Wang, X., Yu, K., Dong, C., & Loy, C. C. (2021). ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 63-79). Springer. Wang X. Yu K. Dong C. Loy C. C. ( 2021 ). ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks . In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 63 - 79 ). Springer . Search in Google Scholar

C. Wang, Z. Li, and J. Shi, “Lightweight Image Super-Resolution with Adaptive Weighted Learning Network,” arXiv preprint arXiv:1904.02358, 2019. Wang C. Li Z. Shi J. Lightweight Image Super-Resolution with Adaptive Weighted Learning Network ,” arXiv preprint arXiv:1904.02358 , 2019 . Search in Google Scholar

Y. Zhang, K. Li, K. Li, L. Wang, B. Zhong, and Y. Fu, “ Residual Feature Aggregation Network for Image Super-Resolution,” in Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2358-2367,2022. Zhang Y. Li K. Li K. Wang L. Zhong B. Fu Y. Residual Feature Aggregation Network for Image Super-Resolution ,” in Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 2358 - 2367 , 2022 . Search in Google Scholar

X. Wang, K. Yu, C. Dong, and C. C. Loy, “Recovering Realistic Texture in Image Super-Resolution by Deep Spatial Feature Transform,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 606–615, 2018. Wang X. Yu K. Dong C. Loy C.C. Recovering Realistic Texture in Image Super-Resolution by Deep Spatial Feature Transform ,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 606 615 , 2018 . Search in Google Scholar

Y. Zhang, K. Li, K. Li, L. Wang, B. Zhong, and Y. Fu, “Image Super-Resolution Using Very Deep Residual Channel Attention Networks,” IEEE Transactions on Patt ern Analysis and Machine Intelligence, vol. 43, no. 10, pp.3551-3567,2021. Zhang Y. Li K. Li K. Wang L. Zhong B. Fu Y. Image Super-Resolution Using Very Deep Residual Channel Attention Networks ,” IEEE Transactions on Patt ern Analysis and Machine Intelligence , vol. 43 , no. 10 , pp. 3551 - 3567 , 2021 . Search in Google Scholar

X. Chen, J. Wang, and Y. Guo, “Dynamic Learning Rate Scheduling for Deep Neural Networks with Cosine Annealing and Warm Restarts, ” Neural Networks, vol. 145, pp. 1–12, 2022. Chen X. Wang J. Guo Y. Dynamic Learning Rate Scheduling for Deep Neural Networks with Cosine Annealing and Warm Restarts , ” Neural Networks , vol. 145 , pp. 1 12 , 2022 . Search in Google Scholar

Z. Wang, J. Chen, and S. C. H. Hoi, “Deep Learning for Image Super-Resolution: A Survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 10, pp. 3365–3387, 2021. Wang Z. Chen J. Hoi S. C. H. Deep Learning for Image Super-Resolution: A Survey ,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 43 , no. 10 , pp. 3365 3387 , 2021 . Search in Google Scholar

Y. Zhang, K. Li, K. Li, L. Wang, B. Zhong, and Y. Fu, “Image Super-Resolution Using Very Deep Residual C hannel Attention Networks,” IEEE Transactions on P-at tern Analysis and Machine Intelligence, vol. 43, no. 10, pp. 3551–3567, 2021. Zhang Y. Li K. Li K. Wang L. Zhong B. Fu Y. Image Super-Resolution Using Very Deep Residual C hannel Attention Networks ,” IEEE Transactions on P-at tern Analysis and Machine Intelligence , vol. 43 , no. 10 , pp. 3551 3567 , 2021 . Search in Google Scholar

Y. Zhang, Y. Tian, Y. Kong, B. Zhong, and Y. Fu, “Residual Dense Network for Image Super-Resolution,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 5, pp. 2480–2495, 2022. Zhang Y. Tian Y. Kong Y. Zhong B. Fu Y. Residual Dense Network for Image Super-Resolution ,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 44 , no. 5 , pp. 2480 2495 , 2022 . Search in Google Scholar

Idioma:
Inglés
Calendario de la edición:
4 veces al año
Temas de la revista:
Informática, Informática, otros