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Super-resolution
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Superresolution for UAV Images via Adaptive Multiple Sparse
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IEEE DOI
1706
Dictionaries, Image edge detection, Image resolution, Imaging,
Monitoring, Training, Unmanned aerial vehicles, 3-D images,
aerial image, agriculture, monitoring, phenotyping,
sparse representation, superresolution (SR), unmanned, aerial,
vehicle, (UAV)
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Zhang, T.[Ting],
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Super-Resolution Reconstruction of Remote Sensing Images Using
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DOI Link
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Wang, P.[Peng],
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Utilizing Pansharpening Technique to Produce Sub-Pixel Resolution
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1806
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Improving Remote Sensing Image Super-Resolution Mapping Based on the
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DOI Link
1902
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Chang, Y.P.[Yun-Peng],
Luo, B.[Bin],
Bidirectional Convolutional LSTM Neural Network for Remote Sensing
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RS(11), No. 20, 2019, pp. xx-yy.
DOI Link
1910
BibRef
Dong, X.Y.[Xiao-Yu],
Xi, Z.H.[Zhi-Hong],
Sun, X.[Xu],
Gao, L.R.[Lian-Ru],
Transferred Multi-Perception Attention Networks for Remote Sensing
Image Super-Resolution,
RS(11), No. 23, 2019, pp. xx-yy.
DOI Link
1912
BibRef
Dong, X.Y.[Xiao-Yu],
Wang, L.G.[Long-Guang],
Sun, X.[Xu],
Jia, X.P.[Xiu-Ping],
Gao, L.R.[Lian-Ru],
Zhang, B.[Bing],
Remote Sensing Image Super-Resolution Using Second-Order Multi-Scale
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GeoRS(59), No. 4, April 2021, pp. 3473-3485.
IEEE DOI
2104
Remote sensing, Image reconstruction, Spatial resolution,
Convolution, Feature extraction, Task analysis, Feature reuse,
super-resolution (SR)
BibRef
Dong, X.Y.[Xiao-Yu],
Sun, X.[Xu],
Jia, X.P.[Xiu-Ping],
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Gao, L.R.[Lian-Ru],
Zhang, B.[Bing],
Remote Sensing Image Super-Resolution Using Novel Dense-Sampling
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GeoRS(59), No. 2, February 2021, pp. 1618-1633.
IEEE DOI
2101
Image reconstruction, Remote sensing, Feature extraction,
Spatial resolution, Convolutional neural networks,
wide activation
See also Multi-Resolution Weed Classification via Convolutional Neural Network and Superpixel Based Local Binary Pattern Using Remote Sensing Images.
BibRef
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1901
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DOI Link
1912
Image processing, Image reconstruction techniques,
Computational imaging, Image processing, Image quality, Signal processing
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Enlighten-GAN for Super Resolution Reconstruction in Mid-Resolution
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DOI Link
2104
BibRef
Zhang, D.Y.[Dong-Yang],
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Shen, H.T.[Heng Tao],
Remote Sensing Image Super-Resolution via Mixed High-Order Attention
Network,
GeoRS(59), No. 6, June 2021, pp. 5183-5196.
IEEE DOI
2106
Remote sensing, Feature extraction, Image resolution,
Image restoration, Image reconstruction, Task analysis, Satellites,
satellite image
BibRef
Peng, Y.[Yali],
Wang, X.N.[Xu-Ning],
Zhang, J.W.[Jun-Wei],
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Pre-training of gated convolution neural network for remote sensing
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IET-IPR(15), No. 5, 2021, pp. 1179-1188.
DOI Link
2106
BibRef
Zhao, M.H.[Ming-Hua],
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Li, T.T.[Ting-Ting],
Hyperspectral Image Super-Resolution under the Guidance of Deep
Gradient Information,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link
2106
BibRef
Huan, H.[Hai],
Li, P.C.[Peng-Cheng],
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Wang, C.[Chao],
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Xie, Y.[Yong],
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End-to-End Super-Resolution for Remote-Sensing Images Using an
Improved Multi-Scale Residual Network,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link
2103
BibRef
Huang, B.[Bo],
He, B.Y.[Bo-Yong],
Wu, L.[Liaoni],
Guo, Z.M.[Zhi-Ming],
Deep Residual Dual-Attention Network for Super-Resolution
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RS(13), No. 14, 2021, pp. xx-yy.
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2107
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Huang, B.[Bo],
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He, B.Y.[Bo-Yong],
Li, X.J.[Xian-Jiang],
Lin, Y.X.[Yu-Xing],
Pyramid Information Distillation Attention Network for
Super-Resolution Reconstruction of Remote Sensing Images,
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link
2112
BibRef
Tao, Y.T.[Yi-Ting],
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RS(9), No. 12, 2017, pp. xx-yy.
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Transferring Deep Convolutional Neural Networks for the Scene
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IEEE DOI
1712
Use small number of labeled pixels.
Data models, Deconvolution, Feature extraction, Image resolution,
Remote sensing, Satellites, Training,
very high resolution (VHR) image per-pixel classification
BibRef
Xu, R.D.[Ru-Dong],
Tao, Y.T.[Yi-Ting],
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Ma, Y.C.[Yun-Chuan],
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Remote sensing image super-resolution based on convolutional blind
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IET-IPR(15), No. 11, 2021, pp. 2508-2520.
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2108
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RS(13), No. 16, 2021, pp. xx-yy.
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2109
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Wang, Z.Y.[Zhao-Yang],
Hu, J.[Jian],
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He, L.[Lihuo],
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2410
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Super-Resolution-Guided Progressive Pansharpening Based on a Deep
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GeoRS(59), No. 6, June 2021, pp. 5206-5220.
IEEE DOI
2106
Spatial resolution, Remote sensing, Neural networks, Dictionaries,
Transforms, Deep learning, multispectral (MS) image,
super-resolution (SR)
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Teo, T.A.[Tee-Ann],
Fu, Y.J.[Yu-Ju],
Spatiotemporal Fusion of Formosat-2 and Landsat-8 Satellite Images:
A Comparison of 'Super Resolution-Then-Blend' and 'Blend-Then-Super
Resolution' Approaches,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link
2103
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Choi, Y.[Yeonju],
Han, S.[Sanghyuck],
Kim, Y.[Yongwoo],
A No-Reference CNN-Based Super-Resolution Method for KOMPSAT-3 Using
Adaptive Image Quality Modification,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link
2109
BibRef
He, Z.[Zhi],
He, D.[Dan],
A Unified Network for Arbitrary Scale Super-Resolution of Video
Satellite Images,
GeoRS(59), No. 10, October 2021, pp. 8812-8825.
IEEE DOI
2109
Satellites, Convolution, Feature extraction, Spatial resolution,
Image reconstruction, Image edge detection, Training,
video satellite
BibRef
Cheng, H.Q.[Hong-Quan],
Wu, H.Y.[Hua-Yi],
Zheng, J.[Jie],
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Liu, W.X.[Wen-Xuan],
A hierarchical self-attention augmented Laplacian pyramid expanding
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PandRS(182), 2021, pp. 52-66.
Elsevier DOI
2112
High-resolution remote sensing image, Change detection,
Convolutional neural network, Self-attention, Laplacian pyramid
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Zhang, N.[Ning],
Wang, Y.C.[Yong-Cheng],
Zhang, X.[Xin],
Xu, D.D.[Dong-Dong],
Wang, X.D.[Xiao-Dong],
Ben, G.L.[Guang-Li],
Zhao, Z.K.[Zhi-Kang],
Li, Z.[Zheng],
A Multi-Degradation Aided Method for Unsupervised Remote Sensing
Image Super Resolution With Convolution Neural Networks,
GeoRS(60), 2022, pp. 1-14.
IEEE DOI
2112
Image resolution, Degradation, Spatial resolution, Kernel,
Image reconstruction, Hyperspectral imaging, unsupervised learning
BibRef
Hou, M.Z.[Ming-Zheng],
He, X.D.[Xu-Dong],
Dou, F.[Furong],
Zhang, X.[Xin],
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Semi-supervised image super-resolution with attention CycleGAN,
IET-IPR(16), No. 4, 2022, pp. 1181-1193.
DOI Link
2203
BibRef
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Wang, X.X.[Xiao-Xu],
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Enhanced Window-Based Self-Attention with Global and Multi-Scale
Representations for Remote Sensing Image Super-Resolution,
RS(16), No. 15, 2024, pp. 2837.
DOI Link
2408
BibRef
Yue, X.C.[Xiu-Chao],
Chen, X.X.[Xiao-Xuan],
Zhang, W.X.[Wan-Xu],
Ma, H.[Hang],
Wang, L.[Lin],
Zhang, J.Y.[Jia-Yang],
Wang, M.W.[Meng-Wei],
Jiang, B.[Bo],
Super-Resolution Network for Remote Sensing Images via
Preclassification and Deep-Shallow Features Fusion,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link
2202
BibRef
Guo, M.Q.[Ming-Qiang],
Zhang, Z.[Zeyuan],
Liu, H.[Heng],
Huang, Y.[Ying],
NDSRGAN: A Novel Dense Generative Adversarial Network for Real Aerial
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RS(14), No. 7, 2022, pp. xx-yy.
DOI Link
2205
BibRef
Cai, Y.X.[Yu-Xi],
Gao, G.[Guxue],
Jia, Z.H.[Zhen-Hong],
Lai, H.C.[Hui-Cheng],
Image Reconstruction of Multibranch Feature Multiplexing Fusion
Network with Mixed Multilayer Attention,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link
2205
BibRef
Xu, Y.Y.[Yong-Yang],
Luo, W.[Wei],
Hu, A.[Anna],
Xie, Z.[Zhong],
Xie, X.J.[Xue-Jing],
Tao, L.F.[Liu-Feng],
TE-SAGAN: An Improved Generative Adversarial Network for Remote
Sensing Super-Resolution Images,
RS(14), No. 10, 2022, pp. xx-yy.
DOI Link
2206
BibRef
Guo, M.Q.[Ming-Qiang],
Xiong, F.[Feng],
Zhao, B.R.[Bao-Rui],
Huang, Y.[Ying],
Xie, Z.[Zhong],
Wu, L.[Liang],
Chen, X.[Xueye],
Zhang, J.[JiaMing],
TDEGAN: A Texture-Detail-Enhanced Dense Generative Adversarial
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RS(16), No. 13, 2024, pp. 2312.
DOI Link
2407
BibRef
Li, Z.Y.[Zhi-Yuan],
Guo, J.Y.[Jia-Yi],
Zhang, Y.T.[Yue-Ting],
Li, J.[Jie],
Wu, Y.R.[Yi-Rong],
Reference-Based Multi-Level Features Fusion Deblurring Network for
Optical Remote Sensing Images,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link
2206
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Dong, R.M.[Run-Min],
Zhang, L.X.[Li-Xian],
Fu, H.H.[Hao-Huan],
RRSGAN: Reference-Based Super-Resolution for Remote Sensing Image,
GeoRS(60), 2022, pp. 1-17.
IEEE DOI
2112
Remote sensing, Image reconstruction, Feature extraction, Earth,
Internet, Superresolution, Deep learning,
super-resolution (SR)
BibRef
Dong, R.M.[Run-Min],
Mou, L.C.[Li-Chao],
Zhang, L.X.[Li-Xian],
Fu, H.H.[Hao-Huan],
Zhu, X.X.[Xiao Xiang],
Real-world remote sensing image super-resolution via a practical
degradation model and a kernel-aware network,
PandRS(191), 2022, pp. 155-170.
Elsevier DOI
2208
Blind super-resolution, Image reconstruct,
Blur-kernel estimation, Deblur, Image degradation, Deep learning
BibRef
Liu, J.Z.[Jin-Zhe],
Yuan, Z.Q.[Zhi-Qiang],
Pan, Z.Y.[Zhao-Ying],
Fu, Y.Q.[Yi-Qun],
Liu, L.[Li],
Lu, B.[Bin],
Diffusion Model with Detail Complement for Super-Resolution of Remote
Sensing,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link
2210
BibRef
Wang, X.[Xuan],
Yi, J.L.[Jing-Lei],
Guo, J.[Jian],
Song, Y.C.[Yong-Chao],
Lyu, J.[Jun],
Xu, J.D.[Jin-Dong],
Yan, W.Q.[Wei-Qing],
Zhao, J.D.[Jin-Dong],
Cai, Q.[Qing],
Min, H.G.[Hai-Gen],
A Review of Image Super-Resolution Approaches Based on Deep Learning
and Applications in Remote Sensing,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link
2212
BibRef
Zhang, Z.L.[Zi-Li],
Tian, Y.[Yan],
Li, J.X.[Jian-Xiang],
Xu, Y.P.[Yi-Ping],
Unsupervised Remote Sensing Image Super-Resolution Guided by Visible
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RS(14), No. 6, 2022, pp. xx-yy.
DOI Link
2204
BibRef
Karwowska, K.[Kinga],
Wierzbicki, D.[Damian],
Improving Spatial Resolution of Satellite Imagery Using Generative
Adversarial Networks and Window Functions,
RS(14), No. 24, 2022, pp. xx-yy.
DOI Link
2212
BibRef
Zhao, J.Y.[Jia-Yi],
Ma, Y.[Yong],
Chen, F.[Fu],
Shang, E.[Erping],
Yao, W.T.[Wu-Tao],
Zhang, S.Y.[Shu-Yan],
Yang, J.[Jin],
SA-GAN: A Second Order Attention Generator Adversarial Network with
Region Aware Strategy for Real Satellite Images Super Resolution
Reconstruction,
RS(15), No. 5, 2023, pp. xx-yy.
DOI Link
2303
BibRef
Zhang, J.Y.[Jia-Yang],
Zhang, W.X.[Wan-Xu],
Jiang, B.[Bo],
Tong, X.D.[Xiao-Dan],
Chai, K.[Keya],
Yin, Y.C.[Yan-Chao],
Wang, L.[Lin],
Jia, J.[Junhao],
Chen, X.X.[Xiao-Xuan],
Reference-Based Super-Resolution Method for Remote Sensing Images
with Feature Compression Module,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link
2303
BibRef
An, T.[Tai],
Huo, C.L.[Chun-Lei],
Xiang, S.M.[Shi-Ming],
Pan, C.H.[Chun-Hong],
Combining Discrete and Continuous Representation:
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RS(15), No. 7, 2023, pp. 1827.
DOI Link
2304
BibRef
Berga, D.[David],
Gallés, P.[Pau],
Takáts, K.[Katalin],
Mohedano, E.[Eva],
Riordan-Chen, L.[Laura],
Garcia-Moll, C.[Clara],
Vilaseca, D.[David],
Marín, J.[Javier],
QMRNet: Quality Metric Regression for EO Image Quality Assessment and
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RS(15), No. 9, 2023, pp. xx-yy.
DOI Link
2305
BibRef
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Yue, L.W.[Lin-Wei],
Zheng, G.Z.[Gui-Zhou],
Cross-Sensor Remote Sensing Imagery Super-Resolution Via an
Edge-Guided Attention-Based Network,
PandRS(199), 2023, pp. 226-241.
Elsevier DOI
2305
Super-resolution, Remote sensing imagery, Degradation modeling, Edge prior
BibRef
Guo, J.F.[Ji-Feng],
Lv, F.C.[Fei-Cai],
Shen, J.Y.[Jia-You],
Liu, J.[Jing],
Wang, M.Z.[Ming-Zhi],
An improved generative adversarial network for remote sensing image
super-resolution,
IET-IPR(17), No. 6, 2023, pp. 1852-1863.
DOI Link
2305
image processing, image reconstruction, image resolution
BibRef
Kong, J.[Juwon],
Ryu, Y.[Youngryel],
Jeong, S.C.[Sung-Chan],
Zhong, Z.L.[Zi-Long],
Choi, W.[Wonseok],
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Jang, K.[Keunchang],
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Elsevier DOI
2306
CubeSat, Deep learning, Generative adversarial network, Landsat,
Super-resolution, Vegetation index
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CGC-Net: A Context-Guided Constrained Network for Remote-Sensing
Image Super Resolution,
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BibRef
Chang, Y.[Yali],
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Chen, J.[Jifa],
Pixel-Wise Attention Residual Network for Super-Resolution of Optical
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RS(15), No. 12, 2023, pp. xx-yy.
DOI Link
2307
BibRef
Han, L.T.[Lin-Tao],
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Liu, H.L.[Hai-Long],
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Enhancing Remote Sensing Image Super-Resolution with Efficient Hybrid
Conditional Diffusion Model,
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BibRef
Shang, J.R.[Jian-Run],
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Hybrid-Scale Hierarchical Transformer for Remote Sensing Image
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RS(15), No. 13, 2023, pp. 3442.
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BibRef
Chung, M.Y.[Mink-Yung],
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Enhancing Remote Sensing Image Super-Resolution Guided by
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RS(15), No. 13, 2023, pp. 3309.
DOI Link
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BibRef
Yue, X.H.[Xiao-Han],
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Benediktsson, J.A.[Jón Atli],
Meng, L.[Linghong],
Deng, L.[Lei],
IESRGAN: Enhanced U-Net Structured Generative Adversarial Network for
Remote Sensing Image Super-Resolution Reconstruction,
RS(15), No. 14, 2023, pp. 3490.
DOI Link
2307
BibRef
Tu, Z.M.[Zi-Ming],
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Tang, X.Y.[Xing-Yu],
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He, X.[Xi],
Liu, P.[Penglin],
Jiang, L.[Li],
Fu, Z.Q.[Zong-Qiang],
AEFormer: Zoom Camera Enables Remote Sensing Super-Resolution via
Aligned and Enhanced Attention,
RS(15), No. 22, 2023, pp. 5409.
DOI Link
2311
BibRef
Hu, C.L.[Chen-Lu],
Ma, M.T.[Meng-Ting],
Ma, X.W.[Xiao-Wen],
Zhang, H.T.[Huan-Ting],
Wu, D.[Dun],
Gao, G.[Guang],
Zhang, W.[Wei],
STANet: Spatiotemporal Adaptive Network for Remote Sensing Images,
ICIP23(3429-3433)
IEEE DOI
2312
BibRef
Xiao, Y.[Yi],
Yuan, Q.Q.[Qiang-Qiang],
Jiang, K.[Kui],
He, J.[Jiang],
Lin, C.W.[Chia-Wen],
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TTST: A Top-k Token Selective Transformer for Remote Sensing Image
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IEEE DOI Code:
WWW Link.
2402
Transformers, Remote sensing, Task analysis, Kernel, Superresolution,
Convolution, Interference, Remote sensing image, super-resolution,
selective attention
BibRef
Wang, L.[Longbao],
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Li, X.[Xin],
Zeng, H.[Hui],
Zhang, H.L.[Hai-Long],
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A CBAM-GAN-based method for super-resolution reconstruction of remote
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2402
image resolution, remote sensing
BibRef
Wasala, J.[Julia],
Marselis, S.[Suzanne],
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AutoSR4EO: An AutoML Approach to Super-Resolution for Earth
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RS(16), No. 3, 2024, pp. 443.
DOI Link
2402
BibRef
Zhang, W.J.[Wen-Jian],
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Li, J.A.[Jia-Ao],
Zhu, B.Y.[Bao-Yu],
Liu, Y.Y.[Yang-Yang],
An Efficient Hybrid CNN-Transformer Approach for Remote Sensing
Super-Resolution,
RS(16), No. 5, 2024, pp. 880.
DOI Link
2403
BibRef
Sui, J.[Jialu],
Wu, Q.Q.[Qian-Qian],
Pun, M.O.[Man-On],
Denoising Diffusion Probabilistic Model with Adversarial Learning for
Remote Sensing Super-Resolution,
RS(16), No. 7, 2024, pp. 1219.
DOI Link
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BibRef
Hu, W.Y.[Wen-Yi],
Ju, L.[Lei],
Du, Y.J.[Yu-Jia],
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A Super-Resolution Reconstruction Model for Remote Sensing Image
Based on Generative Adversarial Networks,
RS(16), No. 8, 2024, pp. 1460.
DOI Link
2405
BibRef
Karwowska, K.[Kinga],
Wierzbicki, D.[Damian],
Modified ESRGAN with Uformer for Video Satellite Imagery
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RS(16), No. 11, 2024, pp. 1926.
DOI Link
2406
BibRef
Guo, Y.D.[Yong-De],
Gong, C.Y.[Cheng-Ying],
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Activated Sparsely Sub-Pixel Transformer for Remote Sensing Image
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RS(16), No. 11, 2024, pp. 1895.
DOI Link
2406
BibRef
Wang, Y.X.[Yin-Xiao],
Yuan, W.[Wei],
Xie, F.[Fang],
Lin, B.J.[Bao-Jun],
ESatSR: Enhancing Super-Resolution for Satellite Remote Sensing
Images with State Space Model and Spatial Context,
RS(16), No. 11, 2024, pp. 1956.
DOI Link
2406
BibRef
Zhao, Z.K.[Zhi-Kang],
Wang, Y.C.[Yong-Cheng],
Zhang, N.[Ning],
Zhang, Y.X.[Yu-Xi],
Li, Z.[Zheng],
Chen, C.[Chi],
A method of degradation mechanism-based unsupervised remote sensing
image super-resolution,
IVC(148), 2024, pp. 105108.
Elsevier DOI
2407
BibRef
And:
Corrigendum:
IVC(151), 2024, pp. 105275.
Elsevier DOI
2411
Super-resolution, Remote sensing, Deep learning,
Unsupervised learning, Degradation mechanism
BibRef
Liu, S.R.[Shi-Rong],
Jia, W.T.[Wen-Tao],
Wang, Q.Y.[Qian-Yun],
Zhang, W.M.[Wei-Min],
Wang, H.Z.[Hui-Zan],
Enhancing the Resolution of Satellite Ocean Data Using Discretized
Satellite Gridding Neural Networks,
RS(16), No. 16, 2024, pp. 3020.
DOI Link
2408
BibRef
Qin, Y.[Yi],
Nie, H.T.[Hai-Tao],
Wang, J.[Jiarong],
Liu, H.Y.[Hui-Ying],
Sun, J.Q.[Jia-Qi],
Zhu, M.[Ming],
Lu, J.[Jie],
Pan, Q.[Qi],
Multi-Degradation Super-Resolution Reconstruction for Remote Sensing
Images with Reconstruction Features-Guided Kernel Correction,
RS(16), No. 16, 2024, pp. 2915.
DOI Link
2408
BibRef
Hao, S.[Shuai],
Liu, S.[Shuai],
Jia, X.[Xu],
Lu, H.C.[Hu-Chuan],
He, Y.[You],
Efficient Adaptive Feature Fusion Network for Remote-Sensing Image
Super-Resolution,
SPLetters(31), 2024, pp. 3089-3093.
IEEE DOI
2411
Feature extraction, Superresolution, Transformers, Remote sensing,
Adaptation models, Data mining, Computational modeling,
image super-resolution
BibRef
Kang, X.D.[Xu-Dong],
Duan, P.[Puhong],
Li, J.[Jier],
Li, S.T.[Shu-Tao],
Efficient Swin Transformer for Remote Sensing Image Super-Resolution,
IP(33), 2024, pp. 6367-6379.
IEEE DOI Code:
WWW Link.
2411
Remote sensing, Superresolution, Feature extraction,
Image reconstruction, Computational modeling,
channel attention
BibRef
Wang, X.[Xuan],
Sun, L.J.[Li-Jun],
Yi, J.L.[Jing-Lei],
Song, Y.C.[Yong-Chao],
Zheng, Q.[Qiang],
Chehri, A.[Abdellah],
Efficient degradation representation learning network for remote
sensing image super-resolution,
CVIU(249), 2024, pp. 104182.
Elsevier DOI Code:
WWW Link.
2412
Representation learning, Remote sensing applications, Attention mechanism
BibRef
Lu, X.Y.[Xiang-Yu],
Zhang, J.L.[Jian-Lin],
Yang, R.[Rui],
Yang, Q.[Qina],
Chen, M.Y.[Meng-Yuan],
Xu, H.X.[Hong-Xing],
Wan, P.[Pinjun],
Guo, J.[Jiawen],
Liu, F.[Fei],
Effective variance attention-enhanced diffusion model for crop field
aerial image super resolution,
PandRS(218), 2024, pp. 50-68.
Elsevier DOI Code:
WWW Link.
2412
Super-resolution, Diffusion model, Variance attention,
Aerial imagery, Super-resolution relative fidelity index
BibRef
Li, Y.H.[Ying-Hua],
Xie, J.Y.[Jing-Yi],
Chi, K.[Kaichen],
Zhang, Y.[Ying],
Dong, Y.Y.[Yun-Yun],
Feature Intensification Using Perception-Guided Regional
Classification for Remote Sensing Image Super-Resolution,
RS(16), No. 22, 2024, pp. 4201.
DOI Link
2412
BibRef
Mao, Y.J.[Yu-Jie],
He, G.J.[Guo-Jin],
Wang, G.Z.[Gui-Zhou],
Yin, R.[Ranyu],
Peng, Y.[Yan],
Guan, B.[Bin],
DESAT: A Distance-Enhanced Strip Attention Transformer for Remote
Sensing Image Super-Resolution,
RS(16), No. 22, 2024, pp. 4251.
DOI Link
2412
BibRef
Wang, X.Y.[Xiao-Ya],
Zhong, B.[Bo],
Ao, K.[Kai],
Du, B.[Bailin],
Hu, L.F.[Long-Fei],
Cai, H.[He],
Qiao, Y.[Yang],
Wu, J.J.[Jun-Jun],
Yang, A.[Aixia],
Wu, S.[Shanlong],
Liu, Q.H.[Qin-Huo],
Reconstruction of 30 m Land Cover in the Qilian Mountains from 1980
to 1990 Based on Super-Resolution Generative Adversarial Networks,
RS(16), No. 22, 2024, pp. 4252.
DOI Link
2412
BibRef
Jiao, D.[Dian],
Su, N.[Nan],
Yan, Y.M.[Yi-Ming],
Liang, Y.[Ying],
Feng, S.[Shou],
Zhao, C.H.[Chun-Hui],
He, G.J.[Guang-Jun],
SymSwin: Multi-Scale-Aware Super-Resolution of Remote Sensing Images
Based on Swin Transformers,
RS(16), No. 24, 2024, pp. 4734.
DOI Link
2501
BibRef
Huang, J.J.[Jia-Jie],
Jiang, W.[Wen],
Liu, J.W.[Jian-Wei],
Xie, Q.[Qinyu],
Li, W.Z.[Wang-Zhe],
A Resolution-Improving Method for Multiband Imaging Based on an
Extrapolated RELAX Algorithm,
RS(16), No. 23, 2024, pp. 4446.
DOI Link
2501
BibRef
Wang, J.[Jie],
Li, H.W.[Hong-Wei],
Li, Y.F.[Yi-Fan],
Qin, Z.L.[Zi-Long],
A Lightweight CNN-Transformer Implemented via Structural
Re-Parameterization and Hybrid Attention for Remote Sensing Image
Super-Resolution,
IJGI(14), No. 1, 2025, pp. 8.
DOI Link
2501
BibRef
Wang, C.[Ce],
Sun, W.J.[Wan-Jie],
Semantic guided large scale factor remote sensing image
super-resolution with generative diffusion prior,
PandRS(220), 2025, pp. 125-138.
Elsevier DOI Code:
WWW Link.
2502
Super-resolution, Remote sensing imagery, Style transfer,
Semantic guidance, Generative model
BibRef
He, X.L.[Xiao-Le],
Liu, P.[Ping],
Wang, J.L.[Jun-Ling],
Performance Boundaries and Tradeoffs in Super-Resolution Imaging
Technologies for Space Targets,
RS(17), No. 4, 2025, pp. 696.
DOI Link
2502
BibRef
Lafenetre, J.[Jamy],
Nguyen, N.L.[Ngoc Long],
Facciolo, G.[Gabriele],
Eboli, T.[Thomas],
Handheld Burst Super-Resolution Meets Multi-Exposure Satellite
Imagery,
EarthVision23(2056-2064)
IEEE DOI
2309
BibRef
Deng, K.[Kai],
Yao, P.[Ping],
Cheng, S.Y.[Si-Yuan],
Bi, J.Y.[Jun-Yu],
Zhang, K.[Kun],
Transformation Consistency for Remote Sensing Image Super-Resolution,
ICIP23(201-205)
IEEE DOI
2312
BibRef
Lin, X.Y.[Xiao-Yu],
Ozaydin, B.[Baran],
Vidit, V.[Vidit],
El Helou, M.[Majed],
Süsstrunk, S.[Sabine],
DSR: Towards Drone Image Super-resolution,
AIM22(361-377).
Springer DOI
2304
BibRef
Yellin, F.[Florence],
Smith, E.[Eric],
Albright, M.[Michael],
McCloskey, S.[Scott],
Resolution Transfer for Object Detection from Satellite Imagery,
ICPR22(449-456)
IEEE DOI
2212
Lower resolution, higher repeat view, small satellites.
Training, Airplanes, Visualization, Image resolution, Annotations,
Small satellites, Training data
BibRef
Wang, S.[Suhe],
Liu, B.[Bo],
Deep Attention-based Lightweight Network For Aerial Image Deblurring,
ICPR22(111-118)
IEEE DOI
2212
Training, Image coding, Computational modeling, Image restoration,
Task analysis, Context modeling
BibRef
Ibrahim, M.R.[Mohamed Ramzy],
Benavente, R.[Robert],
Lumbreras, F.[Felipe],
Ponsa, D.[Daniel],
3DRRDB: Super Resolution of Multiple Remote Sensing Images using 3D
Residual in Residual Dense Blocks,
PBVS22(322-331)
IEEE DOI
2210
Training, Solid modeling, PSNR, Convolution, Superresolution,
Pattern recognition
BibRef
Shacht, G.[Guy],
Danon, D.[Dov],
Fogel, S.[Sharon],
Cohen-Or, D.[Daniel],
Single Pair Cross-Modality Super Resolution,
CVPR21(6374-6383)
IEEE DOI
2111
Image sensors, Visualization, Correlation, Superresolution,
Semantics, Training data, Transformers
BibRef
Bull, D.[Daniel],
Lim, N.[Nick],
Frank, E.[Eibe],
Perceptual improvements for Super-Resolution of Satellite Imagery,
IVCNZ21(1-6)
IEEE DOI
2201
Deep learning, Satellites, Image edge detection, Superresolution,
Neural networks, Sensors, Super-Resolution,
Deep Neural Network
BibRef
Nguyen, N.L.[Ngoc Long],
Anger, J.[Jérémy],
Davy, A.[Axel],
Arias, P.[Pablo],
Facciolo, G.[Gabriele],
Self-Supervised Super-Resolution for Multi-Exposure Push-Frame
Satellites,
CVPR22(1848-1858)
IEEE DOI
2210
BibRef
Earlier:
Self-supervised multi-image super-resolution for push-frame satellite
images,
EarthVision21(1121-1131)
IEEE DOI
2109
Photography, Earth, Satellites, Superresolution, Encoding,
Signal resolution,
Self-& semi-& meta- & unsupervised learning.
Training, Planets, Neural networks,
Computer architecture, Optical imaging
BibRef
Li, Y.H.[Yin-Hao],
Iwamoto, Y.[Yutaro],
Lin, L.F.[Lan-Fen],
Chen, Y.W.[Yen-Wei],
Parallel-connected Residual Channel Attention Network for Remote
Sensing Image Super-resolution,
MLCSA20(18-30).
Springer DOI
2103
BibRef
Shin, C.,
Kim, S.,
Kim, Y.,
From Planetscope To Worldview: Micro-Satellite Image Super-Resolution
With Optimal Transport Distance,
ICIP20(898-902)
IEEE DOI
2011
Degradation, Remote sensing, Satellites, Histograms,
Image resolution, Training, Generators, Micro-satellite image,
degradation learning
BibRef
Zhu, X.,
Talebi, H.,
Shi, X.,
Yang, F.,
Milanfar, P.,
Super-Resolving Commercial Satellite Imagery Using Realistic Training
Data,
ICIP20(498-502)
IEEE DOI
2011
Satellites, Training data, Data models, Kernel, Spatial resolution,
Degradation, Remote sensing, satellite imagery, super-resolution
BibRef
Nair, P.,
Unni, V.S.,
Chaudhury, K.N.,
Hyperspectral Image Fusion Using Fast High-Dimensional Denoising,
ICIP19(3123-3127)
IEEE DOI
1910
hyperspectral image fusion, plug-and-play, regularization,
high-dimensional denoiser
BibRef
Bosch, M.,
Gifford, C.M.,
Rodriguez, P.A.,
Super-Resolution for Overhead Imagery Using DenseNets and Adversarial
Learning,
WACV18(1414-1422)
IEEE DOI
1806
convolution, feedforward neural nets, image resolution,
learning (artificial intelligence), stereo image processing,
Training
BibRef
Khuon, T.,
Rand, R.,
Greer, J.,
Truslow, E.,
Distributed adaptive spectral and spatial sensor fusion for
super-resolution classification,
AIPR12(1-8)
IEEE DOI
1307
expectation-maximisation algorithm
BibRef
Zou, B.[Bin],
Wang, M.[Meicun],
Zhang, J.P.[Jun-Ping],
Zhang, L.[Lamei],
Zhang, Y.[Ye],
Improving spatial resolution for CHANG'E-1 imagery using ARSIS concept
and Pulse Coupled Neural Networks,
ICIP12(2125-2128).
IEEE DOI
1302
BibRef
Hu, W.G.[Wen-Guang],
Hu, T.B.[Ting-Bo],
Wu, T.[Tao],
Zhang, B.[Bo],
Liu, Q.[Qixu],
Sea-surface image super-resolution based on sparse representation,
IASP11(102-107).
IEEE DOI
1112
BibRef
Zomet, A.[Assaf],
Peleg, S.[Shmuel],
Multi-sensor super-resolution,
WACV02(27-31).
IEEE DOI
0303
BibRef
Earlier:
Efficient Super-resolution and Applications to Mosaics,
ICPR00(Vol I: 579-583).
IEEE DOI
0009
BibRef
Earlier:
Applying Super-Resolution to Panoramic Mosaics,
WACV98(286-287).
IEEE DOI
9809
BibRef
Chapter on Motion Analysis -- Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion continues in
Super Resolution for Sentinel Sensors .