Improved Residual Dense Network for Large Scale Super-Resolution via Generative Adversarial Network
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C. H. Chuang, L. W. Tsai, M. S. Deng, J. W. Hsieh and K. C. Fan, “Vehicle license plate recognition using super-resolution technique,” 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS); 2014: IEEE.
G. Gao, D. Zhu, M. Yang, H. Lu, W. Yang and H. Gao, “Face image super-resolution with pose via nuclear norm regularized structural orthogonal procrustes regression,” Neural Computing and Applications. 2020;32(9): 4361-4371.
C. H. Pham, A. Ducournau, R. Fablet and F. Rousseau, “Brain MRI super-resolution using deep 3D convolutional networks,” 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017); 2017: IEEE.
V. H. Patil and D. S. Bormane, “Interpolation for super resolution imaging,” Innovations and Advanced Techniques in Computer and Information Sciences and Engineering: Springer; 2007. p. 483- 489.
W. Shi, J. Caballero, F. Huszár, J. Totz, AP. Aitken, R. Bishop, et al., “Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network,” Proceedings of the IEEE conference on computer vision and pattern recognition; 2016. https://cv-foundation.org/openaccess/content_cvpr_2016/papers/Shi_Real-Time_Single_Image_CVPR_2016_paper.pdf
M. R. Arefin, V. Michalski, P. L. St-Charles, A. Kalaitzis, S. Kim, S. E. Kahou, et al., “Multi-image super-resolution for remote sensing using deep recurrent networks,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops; 2020. https://openaccess.thecvf.com/content_CVPRW_2020/papers/w11/Arefin_Multi-Image_Super-Resolution_for_Remote_Sensing_Using_Deep_Recurrent_Networks_CVPRW_2020_paper.pdf
C. Dong, C. C. Loy and X. Tang, “Accelerating the super-resolution convolutional neural network,” European conference on computer vision; 2016: Springer.
C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, et al., “Photo-realistic single image super-resolution using a generative adversarial network,” Proceedings of the IEEE conference on computer vision and pattern recognition;2017. https://openaccess.thecvf.com/content_cvpr_2017/papers/Ledig_Photo-Realistic_Single_Image_CVPR_2017_paper.pdf
K. He, X. Zhang, S. Ren and J. Sun, “Deep residual learning for image recognition,” Proceedings of the IEEE conference on computer vision and pattern recognition; 2016. https://openaccess.thecvf.com/content_cvpr_2016/papers/He_Deep_Residual_Learning_CVPR_2016_paper.pdf
W. Ma, Z. Pan, F. Yuan and B. Lei, “Super-resolution of remote sensing images via a dense residual generative adversarial network,” Remote Sensing. 2019;11(21):2578.
J. Kim, J. K. Lee and K. M. Lee, “Deeply-recursive convolutional network for image super-resolution,” Proceedings of the IEEE conference on computer vision and pattern recognition; 2016. https://openaccess.thecvf.com/content_cvpr_2016/papers/Kim_Deeply-Recursive_Convolutional_Network_CVPR_2016_paper.pdf
C. Dong, C. C. Loy, K. He and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE transactions on pattern analysis and machine intelligence. 2015;38(2):295-307. https://arxiv.org/pdf/1501.00092.pdf
Y. Tai, J. Yang and X. Liu, “Image super-resolution via deep recursive residual network,” Proceedings of the IEEE conference on computer vision and pattern recognition; 2017. https://openaccess.thecvf.com/content_cvpr_2017/papers/Tai_Image_Super-Resolution_via_CVPR_2017_paper.pdf
M. Zhao, X. Liu, H. Liu and K. K. L. Wong, “Super-resolution of cardiac magnetic resonance images using Laplacian pyramid based on generative adversarial networks,” Computerized Medical Imaging and Graphics. 2020; 80:101698.
Y. Yu, X. Li and F. Liu, “E-DBPN: Enhanced deep back-projection networks for remote sensing scene image super resolution,” IEEE Transactions on Geoscience and Remote Sensing. 2020;58(8):5503- 5515.
X. Mao, C. Shen and Y. B. Yang, “Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections,” arXiv preprint arXiv:160309056. 2016.
J. Kim, J. K. Lee and K. M. Lee, “Accurate image super-resolution using very deep convolutional networks,” Proceedings of the IEEE conference on computer vision and pattern recognition; 2016. https://openaccess.thecvf.com/content_cvpr_2016/papers/Kim_Accurate_Image_Super-Resolution_CVPR_2016_paper.pdf
Y. Zhang, K. Li, K. Li, L. Wang, B. Zhong and Y. Fu, “Image super-resolution using very deep residual channel attention networks,” Proceedings of the European conference on computer vision (ECCV);2018. https://openaccess.thecvf.com/content_ECCV_2018/papers/Yulun_Zhang_Image_Super-Resolution_Using_ECCV_2018_paper.pdf
J. M. Haut, R. Fernandez-Beltran, M. E. Paoletti, J. Plaza and A. Plaza, “Remote sensing image superresolution using deep residual channel attention,” IEEE Transactions on Geoscience and Remote Sensing. 2019;57[11]:9277-9289.
B. Lim, S. Son, H. Kim, S. Nah and K. Mu Lee, “Enhanced deep residual networks for single image super-resolution,” Proceedings of the IEEE conference on computer vision and pattern recognition workshops; 2017. https://openaccess.thecvf.com/content_cvpr_2017_workshops/w12/papers/Lim_Enhanced_Deep_Residual_CVPR_2017_paper.pdf
Y. Tai, J. Yang, X. Liu and C. Xu, “Memnet: A persistent memory network for image restoration,” Proceedings of the IEEE international conference on computer vision; 2017.
D. W. Chen and C. H. Kuo, “Modified Dual Path Network With Transform Domain Data for Image Super-Resolution,” IEEE Access. 2020;8: 97975-97985.
K. Jiang, Z. Wang, P. Yi and J. Jiang, “Hierarchical dense recursive network for image super-resolution,” Pattern Recognition. 2020; 107:107475.
K. Nazeri, H. Thasarathan and M. Ebrahimi, “Edge-informed single image super-resolution,” Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops; 2019. https://openaccess.thecvf.com/content_ICCVW_2019/papers/AIM/Nazeri_Edge-Informed_Single_Image_Super-Resolution_ICCVW_2019_paper.pdf
J. Ma, X. Wang and J. Jiang, “Image super resolution via dense discriminative network,” IEEE Transactions on Industrial Electronics. 2019;67(7): 5687-5695.
Y. Wang, L. Wang, H. Wang and P. Li, “End-to-end image super-resolution via deep and shallow convolutional networks,” IEEE Access. 2019;7: 31959-31970.
D. Chen, Z. He, Y. Cao, J. Yang, Y. Cao, M. Y. Yang, et al, “Deep Neural Network for Fast and Accurate Single Image Super-Resolution via Channel-Attention-based Fusion of Orientation-aware Features,” arXiv preprint arXiv:191204016. 2019. https://arxiv.org/pdf/1912.04016.pdf
T. Shang, Q. Dai, S. Zhu, T. Yang and Y. Guo, “Perceptual extreme super-resolution network with receptive field block,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops; 2020. https://openaccess.thecvf.com/content_CVPRW_2020/papers/w31/Shang_Perceptual_Extreme_Super-Resolution_Network_With_Receptive_Field_Block_CVPRW_2020_paper.pdf
J. Johnson, A. Alahi and L. Fei-Fei, “Perceptual losses for real-time style transfer and super-resolution,” European conference on computer vision; 2016: Springer.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:14091556. 2014. https://arxiv.org/pdf/1409.1556.pdf(2014.pdf
K. He, X. Zhang, S. Ren and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,” Proceedings of the IEEE international conference on computer vision; 2015. https://openaccess.thecvf.com/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf
X. Wang, K. Yu, S. Wu, J. Gu, Y. Liu, C. Dong, et al., “Esrgan: Enhanced super-resolution generative adversarial networks,” Proceedings of the European Conference on Computer Vision (ECCV) Workshops; 2018. https://openaccess.thecvf.com/content_ECCVW_2018/papers/11133/Wang_ESRGAN_Enhanced_Super-Resolution_Generative_Adversarial_Networks_ECCVW_2018_paper.pdf
B. Xu, N. Wang, T. Chen and M. Li, “Empirical evaluation of rectified activations in convolutional network,” arXiv preprint arXiv:150500853. 2015. https://arxiv.org/pdf/1505.00853.pdf%E3%80%82ReLU
A. Jolicoeur-Martineau, “The relativistic discriminator: a key element missing from standard GAN,” arXiv preprint arXiv:180700734. 2018. https://arxiv.org/pdf/1807.00734.pdf
D. Lee, S. Lee, H. Lee, K. Lee and H. J. Lee, “Resolution-preserving generative adversarial networks for image enhancement,” IEEE Access. 2019;7: 110344-110357.
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin and A. Courville, “Improved training of wasserstein gans,” arXiv preprint arXiv:170400028. 2017. https://arxiv.org/pdf/1704.00028.pdf]
K. Jiang, Z. Wang, P. Yi, G. Wang, T. Lu and J. Jiang, “Edge-enhanced GAN for remote sensing image superresolution,” IEEE Transactions on Geoscience and Remote Sensing. 2019; 57(8):5799-5812.
C. Ma, Y. Rao, Y. Cheng, C. Chen, J. Lu and J. Zhou, “Structure-preserving super resolution with gradient guidance,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition; 2020. https://openaccess.thecvf.com/content_CVPR_2020/papers/Ma_Structure-Preserving_Super_Resolution_With_Gradient_Guidance_CVPR_2020_paper.pdf
Y. Zhang, Y. Tian, Y. Kong, B. Zhong and Y. Fu, “Residual dense network for image super-resolution,” Proceedings of the IEEE conference on computer vision and pattern recognition; 2018. https://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_Residual_Dense_Network_CVPR_2018_paper.pdf
X. Dong, X. Sun, X. Jia, Z. Xi, L. Gao and B. Zhang, “Remote sensing image super-resolution using novel dense-sampling networks,” IEEE Transactions on Geoscience and Remote Sensing. 2020;59(2):1618-1633.
Y. Yang and S. Newsam, “Bag-of-visual-words and spatial extensions for land-use classification,” Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems; 2010. https://faculty.ucmerced.edu/snewsam/papers/Yang_ACMGIS10_BagOfVisualWords.pdf
D. Dai and W. Yang, “Satellite image classification via two-layer sparse coding with biased image representation,” IEEE Geoscience and Remote Sensing Letters. 2010;8(1):173-176. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.703.6870&rep=rep1&type=pdf
Q. Zou, L. Ni, T. Zhang and Q. Wang, “Deep learning based feature selection for remote sensing scene classification,” IEEE Geoscience and Remote Sensing Letters.. 2015;12[11]: 2321-2325. http://mvr.whu.edu.cn/pubs/2015-IEEE_GRSL.pdf
D. Zhang, J. Shao, X. Li and H. T. Shen, “Remote sensing image super-resolution via mixed high-order attention network,” IEEE Transactions on Geoscience and Remote Sensing. 2020;59(6): 5183-5196.