An improved generative adversarial networks for remote sensing image super-resolution reconstruction via multi-scale residual block

Fuzhen Zhu1, Chen Wang1, Bing Zhu2, Ce Sun1, Chengxiao Qi1
1Key laboratory of Remote Sensing Image Processing, Electronic Engineering College, Heilongjiang University, Harbin 150080, PR China
2Institute of Image Information Technology and Engineering, Harbin Institute of Technology, Heilongjiang, Harbin 150001, PR China

Tài liệu tham khảo

Ahn, N., Kang, B., Sohn, K.A., 2018. Fast, accurate, and lightweight super-resolution with cascading residual network, in: Proceedings of the European conference on computer vision (ECCV), pp. 252–268. Berger, 1994, An overview of robust bayesian analysis, Test, 3, 5, 10.1007/BF02562676 Chen, 2022, Feature fusion and kernel selective in inception-v4 network, Applied Soft Computing, 119, 10.1016/j.asoc.2022.108582 Chen, H., Wang, Y., Xu, C., et al., 2020. Addernet: Do we really need multiplications in deep learning?, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 1468–1477. Diniz, 1997, volume 4 Dong, 2015, Image super-resolution using deep convolutional networks, IEEE transactions on pattern analysis and machine intelligence, 38, 295, 10.1109/TPAMI.2015.2439281 Es-SAFI, 2016, HARCHLI: Adaptation of multilayer perceptron neural network to unsupervised clustering using a developed version of k-means algorithm, WSEAS Transactions on Computers, 15, 103 Euijeong, 2021, Srps–deep-learning-based photometric stereo using superresolution images, Journal of Computational Design and Engineering, 4 Gao, 2019, Res2net: A new multi-scale backbone architecture, IEEE transactions on pattern analysis and machine intelligence, 43, 652, 10.1109/TPAMI.2019.2938758 Glasner, D., Bagon, S., Irani, M., 2009. Super-resolution from a single image, in: 2009 IEEE 12th international conference on computer vision, IEEE. pp. 349–356. Gomes, 2019, Machine learning for streaming data: state of the art, challenges, and opportunities, ACM SIGKDD Explorations Newsletter, 21, 6, 10.1145/3373464.3373470 Guo, R., Shi, X.P., Jia, D.K., 2018. Learning a deep convolutional network for image super-resolution reconstruction. Journal of Engineering of Heilongjiang University. Hou, 2020, A novel and effective image super-resolution reconstruction technique via fast global and local residual learning model, Applied Sciences, 10, 1856, 10.3390/app10051856 Huang, G., Liu, S., Van der Maaten, L., et al., 2018. Condensenet: An efficient densenet using learned group convolutions, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2752–2761. Kim, J., Lee, J.K., Lee, K.M., 2016a. Accurate image super-resolution using very deep convolutional networks, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1646–1654. Kim, J., Lee, J.K., Lee, K.M., 2016b. Deeply-recursive convolutional network for image super-resolution, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1637–1645. Ledig, C., Theis, L., Huszár, F., et al., 2017. Photo-realistic single image super-resolution using a generative adversarial network, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4681–4690. Lee, 2020, Learning with privileged information for efficient image super-resolution, 465 Li, 2020, Learning a deep dual attention network for video super-resolution, IEEE transactions on image processing, 29, 4474, 10.1109/TIP.2020.2972118 Li, J., Fang, F., Mei, K., et al., 2018. Multi-scale residual network for image super-resolution, in: Proceedings of the European conference on computer vision (ECCV), pp. 517–532. Li, 2020, Sacnn: Self-attention convolutional neural network for low-dose ct denoising with self-supervised perceptual loss network, IEEE transactions on medical imaging, 39, 2289, 10.1109/TMI.2020.2968472 Liu, 2020, Lightweight multi-scale residual networks with attention for image super-resolution, Knowledge-Based Systems, 203, 10.1016/j.knosys.2020.106103 Liu, 2019, An efficient residual learning neural network for hyperspectral image superresolution, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12, 1240, 10.1109/JSTARS.2019.2901752 Ma, C., Rao, Y., Cheng, Y., et al., 2020. Structure-preserving super resolution with gradient guidance, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 7769–7778. Nedić, A., 2010. Random projection algorithms for convex set intersection problems, in: 49th IEEE Conference on Decision and Control (CDC), IEEE. pp. 7655–7660. Olshausen, 2004, Sparse coding of sensory inputs, Current opinion in neurobiology, 14, 481, 10.1016/j.conb.2004.07.007 Ouyang, 2019, Ultra-low-dose pet reconstruction using generative adversarial network with feature matching and task-specific perceptual loss, Medical physics, 46, 3555, 10.1002/mp.13626 Peng, X., Yongping, L.I., Zhang, X., 2016. Binocular stereo matching algorithm based on deep learning. Qin, 2020, Multi-scale feature fusion residual network for single image super-resolution, Neurocomputing, 379, 334, 10.1016/j.neucom.2019.10.076 Rojo-Álvarez, 2007, Nonuniform interpolation of noisy signals using support vector machines, IEEE Transactions on Signal Processing, 55, 4116, 10.1109/TSP.2007.896029 Shi, 2018, Super-resolution reconstruction of mr image with a novel residual learning network algorithm, Physics in Medicine & Biology, 63, 10.1088/1361-6560/aab9e9 Shi, W., Caballero, J., Huszár, F., et al., 2016. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1874–1883. Wang, X., Yu, K., Wu, S., et al., 2018. Esrgan: Enhanced super-resolution generative adversarial networks, in: Proceedings of the European conference on computer vision (ECCV) workshops, pp. 0–0. Wang, Y., Ying, X., 2021. Symmetric parallax attention for stereo image super-resolution, in: Computer Vision and Pattern Recognition. Woo, S., Park, J., Lee, J.Y., et al., 2018. Cbam: Convolutional block attention module, in: Proceedings of the European conference on computer vision (ECCV), pp. 3–19. Wunsch, P., Hirzinger, G., 1996. Registration of cad-models to images by iterative inverse perspective matching, in: Proceedings of 13th International Conference on Pattern Recognition, IEEE. pp. 78–83. Yang, 2018, Low-dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss, IEEE transactions on medical imaging, 37, 1348, 10.1109/TMI.2018.2827462 Zamir, 2020, Learning enriched features for real image restoration and enhancement, 492 Zeng, Y., Fu, J., Chao, H., et al., 2019. Learning pyramid-context encoder network for high-quality image inpainting, pp. 1486–1494. Zhang, T., Gu, Y., Huang, X., 2020. Stereo endoscopic image super-resolution using disparity-constrained parallel attention. Zhang, Y., Li, K., Li, K., et al., 2018. Image super-resolution using very deep residual channel attention networks, in: Proceedings of the European conference on computer vision (ECCV), pp. 286–301. Zhou, 2016, Image super-resolution via sparse representation, Computer Engineering and Design