Dual-branche attention network for super-resolution of remote sensing images

References

• Agustsson, E., and R. Timofte. 2017. “NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study.” In 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, Hawaii, USA, 1122–1131.
   Google Scholar
• Ahn, N., B. Kang, and K.A. Sohn. 2018. “Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network.” ArXiv:1803.08664 [Cs].
   Google Scholar
• Burt, P. J., and E. H. Adelson. 1987. “The Laplacian Pyramid as a Compact Image Code.” Readings in Computer Vision 31 (4): 671–679.
   Google Scholar
• Chen, H., X. He, L. Qing, and Q. Teng. 2017. “Single Image Super-Resolution via Adaptive Transform-Based Nonlocal Self-Similarity Modeling and Learning-Based Gradient Regularization.” IEEE Transactions on Multimedia 19 (8): 1702–1717. doi:10.1109/TMM.2017.2688920.
   Web of Science ®Google Scholar
• Dai, T., J. Cai, Y. Zhang, S. T. Xia, and L. Zhang. 2019. “Second-Order Attention Network for Single Image Super-Resolution.” In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.
   Google Scholar
• Dai, D., and W. Yang. 2011. “Satellite Image Classification via Two-Layer Sparse Coding with Biased Image Representation.” IEEE Geoscience and Remote Sensing Letters 8 (1): 173–176. doi:10.1109/LGRS.2010.2055033.
   Web of Science ®Google Scholar
• Dong, C., C. Change Loy, H. Kaiming, and X. Tang. 2016. “Image Super-Resolution Using Deep Convolutional Networks.” IEEE Transactions on Pattern Analysis and Machine Intelligence 38 (2): 295–307. doi:10.1109/tpami.2015.2439281.
   PubMed Web of Science ®Google Scholar
• Dong, X., X. Sun, X. Jia, X. Zhihong, L. Gao, and B. Zhang. 2021. “Remote Sensing Image Super-Resolution Using Novel Dense-Sampling Networks.” IEEE Transactions on Geoscience and Remote Sensing 59 (2): 1618–1633. doi:10.1109/TGRS.2020.2994253.
   Web of Science ®Google Scholar
• Dong, X., L. Wang, X. Sun, X. Jia, L. Gao, and B. Zhang. 2021. “Remote Sensing Image Super-Resolution Using Second-Order Multi-Scale Networks.” IEEE Transactions on Geoscience and Remote Sensing 59 (4): 3473–3485. doi:10.1109/TGRS.2020.3019660.
   Web of Science ®Google Scholar
• Dong, X., X. Zhihong, X. Sun, and L. Gao. 2019. “Transferred Multi-Perception Attention Networks for Remote Sensing Image Super-Resolution.” Remote Sensing 11 (23): 2857. doi:10.3390/rs11232857.
   Web of Science ®Google Scholar
• Dou, Q., S. Wei, X. Yang, W. Wu, and K. Liu. 2018. “Medical Image Super-Resolution via Minimum Error Regression Model Selection Using Random Forest.” Sustainable Cities & Society 42: 1–12. doi:10.1016/j.scs.2018.05.028.
   Web of Science ®Google Scholar
• Freedman, G., and R. Fattal. 2011. “Image and Video Upscaling from Local Self-Examples.” ACM Transactions on Graphics 30 (2): 1–11. doi:10.1145/1944846.1944852.
   Web of Science ®Google Scholar
• Hou, B., K. Zhou, and L. Jiao. 2017. “Adaptive Super-Resolution for Remote Sensing Images Based on Sparse Representation with Global Joint Dictionary Model.” IEEE Transactions on Geoence & Remote Sensing 1–16. doi:10.1109/TGRS.2017.2778191.
   Web of Science ®Google Scholar
• Jiang, K., Z. Wang, Y. Peng, G. Wang, L. Tao, and J. Jiang. 2019. “Edge-Enhanced GAN for Remote Sensing Image Superresolution.” IEEE Transactions on Geoscience and Remote Sensing 57 (8): 5799–5812. doi:10.1109/TGRS.2019.2902431.
   Web of Science ®Google Scholar
• Jian, S., Z. Xu, and H. Y. Shum. 2008. “Image Super-Resolution Using Gradient Profile Prior.” In 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008), 24-26 June 2008, Anchorage, Alaska, USA.
   Google Scholar
• Jie, H., L. Shen, and G. Sun. 2018. “Squeeze-And-Excitation Networks.” In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 7132–7141.
   Google Scholar
• Jun, G., X. Sun, Y. Zhang, F. Kun, and L. Wang. 2019. “Deep Residual Squeeze and Excitation Network for Remote Sensing Image Super-Resolution.” Remote Sensing 11 (15): 1817. doi:10.3390/rs11151817.
   Web of Science ®Google Scholar
• Kasetkasem, T., M. K. Arora, and P. K. Varshney. 2005. “Super-Resolution Land Cover Mapping Using a Markov Random Field Based Approach.” Remote Sensing of Environment 96 (3–4): 302–314. doi:10.1016/j.rse.2005.02.006.
   Web of Science ®Google Scholar
• Kim, J., J. K. Lee, and K. M. Lee. 2016. “Accurate Image Super-Resolution Using Very Deep Convolutional Networks.” In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 1646–1654.
   Google Scholar
• Lan, M., Y. Zhang, L. Zhang, and B. Du. 2020. “Global Context Based Automatic Road Segmentation via Dilated Convolutional Neural Network.” Information Sciences 535: 156–171. doi:10.1016/j.ins.2020.05.062.
   Web of Science ®Google Scholar
• Ledig, C., L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, et al. 2017. “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network.” ArXiv:1609.04802 [Cs, Stat].
   Google Scholar
• Li, T. 2012. Image Super-Resolution Using Sharpened Gradient Profile Prior. Southern Methodist University, Dallas, TX, USA: Dissertations & Theses - Gradworks.
   Google Scholar
• Li, F., H. Bai, and Y. Zhao. 2020. “Learning a Deep Dual Attention Network for Video Super-Resolution.” IEEE Transactions on Image Processing 29: 4474–4488. doi:10.1109/TIP.2020.2972118.
   Web of Science ®Google Scholar
• Lim, S. S., H. Kim, S. Nah, and K. Mu Lee. 2017. “Enhanced Deep Residual Networks for Single Image Super-Resolution.” In 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, Hawaii, USA, 1132–1140.
   Google Scholar
• Lim, B., S. Son, H. Kim, S. Nah, and K. M. Lee. 2017. “Enhanced Deep Residual Networks for Single Image Super-Resolution.” IEEE. doi:10.1109/CVPRW.2017.151.
   Google Scholar
• Liu, H., and F. Cao. 2020. “Improved Dual-Scale Residual Network for Image Super-Resolution.” Neural Networks 132: 84–95. doi:10.1016/j.neunet.2020.08.008.
   PubMed Web of Science ®Google Scholar
• Liu, S., D. Huang, and Y. Wang. 2018. “Receptive Field Block Net for Accurate and Fast Object Detection.” ArXiv:1711.07767 [Cs].
   Google Scholar
• Liu, S., J. Pan, and M.H. Yang. 2016. “Learning Recursive Filters for Low-Level Vision via a Hybrid Neural Network.” In Computer Vision – ECCV 2016, B. Leibe, J. Matas, N. Sebe, and M. Welling edited by, Vol. 9908, 560–576. Lecture Notes in Computer Science. Cham: Springer International Publishing.
   Google Scholar
• Liu, Y., Y. Wang, L. Nan, X. Cheng, Y. Zhang, Y. Huang, and L. Guojun. 2018. “An Attention-Based Approach for Single Image Super Resolution.” ArXiv:1807.06779 [Cs].
   Google Scholar
• Li, X., W. Wang, H. Xiaolin, and J. Yang. 2019. “Selective Kernel Networks.” ArXiv:1903.06586 [Cs].
   Google Scholar
• Li, Z., J. Yang, Z. Liu, X. Yang, G. Jeon, and W. Wei 2019. “Feedback Network for Image Super-Resolution.” In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 3862–3871.
   Google Scholar
• Lu, T., J. Wang, Y. Zhang, Z. Wang, and J. Jiang. 2019. “Satellite Image Super-Resolution via Multi-Scale Residual Deep Neural Network.” Remote Sensing 11 (13): 1588. doi:10.3390/rs11131588.
   Web of Science ®Google Scholar
• Lu, Y., Y. Zhou, Z. Jiang, X. Guo, and Z. Yang. 2018. “Channel Attention and Multi-Level Features Fusion for Single Image Super-Resolution.” ArXiv:1810.06935 [Cs].
   Google Scholar
• Lv, P., W. Wenjun, Y. Zhong, D. Fang, and L. Zhang. 2022. “SCViT: A Spatial-Channel Feature Preserving Vision Transformer for Remote Sensing Image Scene Classification.” IEEE Transactions on Geoscience and Remote Sensing 60: 1–12. doi:10.1109/TGRS.2022.3157671.
   Web of Science ®Google Scholar
• Ma, X., J. Guo, S. Tang, Z. Qiao, Q. Chen, Q. Yang, and S. Fu. 2020. “DCANet: Learning Connected Attentions for Convolutional Neural Networks.”
   Google Scholar
• Ma, W., Z. Pan, J. Guo, and B. Lei. 2019. “Achieving Super-Resolution Remote Sensing Images via the Wavelet Transform Combined with the Recursive Res-Net.” IEEE Transactions on Geoscience and Remote Sensing 99 (6): 1–16. doi:10.1109/TGRS.2018.2885506.
   Google Scholar
• Ma, W., Z. Pan, F. Yuan, and B. Lei. 2019. “Super-Resolution of Remote Sensing Images via a Dense Residual Generative Adversarial Network.” Remote Sensing 11 (21): 2578. doi:10.3390/rs11212578.
   Web of Science ®Google Scholar
• Ma, C., Y. Rao, Y. Cheng, C. Chen, L. Jiwen, and J. Zhou. 2020. “Structure-Preserving Super Resolution with Gradient Guidance.” ArXiv:2003.13081 [Cs, Eess].
   Google Scholar
• Matikainen, L., K. Karila, J. Hyyppä, P. Litkey, E. Puttonen, and E. Ahokas. 2017. “Object-Based Analysis of Multispectral Airborne Laser Scanner Data for Land Cover Classification and Map Updating.” Isprs Journal of Photogrammetry and Remote Sensing 128: 298–313. doi:10.1016/j.isprsjprs.2017.04.005.
   Web of Science ®Google Scholar
• Nasser, S. A., S. Shamsi, V. Bundele, B. Garg, and A. Sethi. 2022. “Perceptual CGAN for MRI Super-Resolution.” In 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 3035–3038. Glasgow, Scotland, United Kingdom: IEEE.
   Google Scholar
• Ota, J., K. Umehara, N. Ishimaru, S. Ohno, K. Okamoto, T. Suzuki, M. A. Styner, E. D. Angelini, N. Shirai, and T. Ishida. 2017. “Evaluation of the Sparse Coding Super-Resolution Method for Improving Image Quality of Up-Sampled Images in Computed Tomography.” In Medical Imaging 2017: Image Processing, 509–517 (Vol. 10133). SPIE.
   Google Scholar
• Shang, T., Q. Dai, S. Zhu, T. Yang, and Y. Guo. 2020. “Perceptual Extreme Super Resolution Network with Receptive Field Block.” In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 1778–1787 Seattle, WA, USA: IEEE.
   Google Scholar
• Shao, Z., L. Wang, Z. Wang, and J. Deng. 2019. “Remote Sensing Image Super-Resolution Using Sparse Representation and Coupled Sparse Autoencoder.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12 (8): 2663–2674. doi:10.1109/JSTARS.2019.2925456.
   Web of Science ®Google Scholar
• Shengyang, D., H. Mei, W. Xu, Y. Wu, Y. Gong, and A. K. Katsaggelos. 2009. “SoftCuts: A Soft Edge Smoothness Prior for Color Image Super-Resolution.” IEEE Transactions on Image Processing 18 (5): 969–981. doi:10.1109/TIP.2009.2012908.
   PubMed Web of Science ®Google Scholar
• Shi, W., J. Caballero, F. Huszár, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang. 2016. “Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network.” ArXiv:1609.05158 [Cs, Stat].
   Google Scholar
• Sun, J., X. Zongben, and H.Y. Shum. 2011. “Gradient Profile Prior and Its Applications in Image Super-Resolution and Enhancement.” IEEE Transactions on Image Processing 20 (6): 1529–1542. doi:10.1109/TIP.2010.2095871.
   PubMed Web of Science ®Google Scholar
• Tai, Y. W., S. Liu, M. S. Brown, and S. Lin. 2010. “Super Resolution Using Edge Prior and Single Image Detail Synthesis” In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, San Francisco, California, USA.
   Google Scholar
• Wang, P., P. Chen, Y. Yuan, D. Liu, Z. Huang, X. Hou, and G. Cottrell. 2018. “Understanding Convolution for Semantic Segmentation.“ In 2018 IEEE winter conference on applications of computer vision (WACV) (pp. 1451-1460). Ieee.
   Google Scholar
• Wang, C.Y., I.H.Y. Hong-Yuan Mark Liao, W. Yueh-Hua, P.Y. Chen, and J.W. Hsieh. 2019. “CSPNet: A New Backbone That Can Enhance Learning Capability of CNN.” ArXiv:1911.11929 [Cs]:.
   Google Scholar
• Widiyanto, S., D. Sundani, Y. Karyanti, and D. T. Wardani. 2019. “Edge Detection Based on Quantum Canny Enhancement for Medical Imaging.” IOP Conference Series: Materials Science and Engineering 536 (1): 012118. doi:10.1088/1757-899X/536/1/012118.
   Google Scholar
• Woo, S., J. Park, J.Y. Lee, and I. So Kweon. 2018. “CBAM: Convolutional Block Attention Module.” In Computer Vision – ECCV 2018, V. Ferrari, M. Hebert, C. Sminchisescu, and Y. Weiss edited by, Vol. 11211, 3–19. Lecture Notes in Computer Science. Cham: Springer International Publishing.
   Google Scholar
• Xia, G., J. Hu, F. Hu, B. Shi, X. Bai, Y. Zhong, L. Zhang, and X. Lu. 2017. “AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification.” IEEE Transactions on Geoscience and Remote Sensing 55 (7): 3965–3981. doi:10.1109/TGRS.2017.2685945.
   Web of Science ®Google Scholar
• Yan, Q., Y. Xu, X. Yang, and T. Q. Nguyen. 2015. “Single Image Superresolution Based on Gradient Profile Sharpness.” IEEE Transactions on Image Processing 24 (10): 3187–3202. doi:10.1109/TIP.2015.2414877.
   PubMed Web of Science ®Google Scholar
• Zhang, Y., L. Kunpeng, L. Kai, L. Wang, B. Zhong, and F. Yun. 2018. “Image Super-Resolution Using Very Deep Residual Channel Attention Networks.” In Computer Vision – ECCV 2018, V. Ferrari, M. Hebert, C. Sminchisescu, and Y. Weiss edited by, Vol. 11211, 294–310. Lecture Notes in Computer Science. Cham: Springer International Publishing.
   Google Scholar
• Zhang, D., J. Shao, L. Xinyao, and H. Tao Shen. 2021. “Remote Sensing Image Super-Resolution via Mixed High-Order Attention Network.” IEEE Transactions on Geoscience and Remote Sensing 59 (6): 5183–5196. doi:10.1109/TGRS.2020.3009918.
   Web of Science ®Google Scholar
• Zhang, X., L. Zhenyu, T. Zhang, F. Liu, X. Tang, P. Chen, and L. Jiao. 2022. “Remote Sensing Image Super-Resolution via Dual-Resolution Network Based on Connected Attention Mechanism.” IEEE Transactions on Geoscience and Remote Sensing 60: 1–13. doi:10.1109/TGRS.2021.3106681.
   Web of Science ®Google Scholar
• Zhou Wang, A. C. B., H. R. Sheikh, and E. P. Simoncelli. 2004. “Image Quality Assessment: From Error Visibility to Structural Similarity.” IEEE Transactions on Image Processing 13 (4): 600–612. doi:10.1109/TIP.2003.819861.
   PubMed Web of Science ®Google Scholar
• Zou, Q., N. Lihao, T. Zhang, and Q. Wang. 2015. “Deep Learning Based Feature Selection for Remote Sensing Scene Classification.” IEEE Geoscience and Remote Sensing Letters 12 (11): 1–5. doi:10.1109/LGRS.2015.2475299.
   Web of Science ®Google Scholar
• Zou, X., F. Xiao, Y. Zhiding, L. Yuheng, and Y. Jae Lee. 2022. “Delving Deeper into Anti-Aliasing in ConvNets.” International Journal of Computer Vision 131 (1): 67–81. doi:10.1007/s11263-022-01672-y.
   Web of Science ®Google Scholar