19.4.3.23 Super Resolution for Remote Sensing Applications

Chapter Contents (Back)
Super Resolution. Restoration. Remote Sensing.
See also Super Resolution for Hyperspectral Data.
See also Image and Sensor Fusion for Cartography and Aerial Images, Satellite Images, Remote Sensing.
See also Super Resolution for Sentinel Sensors. Clearly some overlap:
See also Pansharpening, Fusion of Aerial Images.

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Rigorously combine multiple low-resolution digital images into a higher resolution composite, apply to photogrammetric DEM generation. BibRef

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Long, D.G., Spencer, M.W., Njoku, E.G.,
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Akgun, T., Altunbasak, Y., Mersereau, R.M.,
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Zhang, H.Y.[Hong-Yan], Yang, Z.Y.[Ze-Yu], Zhang, L.P.[Liang-Pei], Shen, H.F.[Huan-Feng],
Super-Resolution Reconstruction for Multi-Angle Remote Sensing Images Considering Resolution Differences,
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DOI Link 1402
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Lv, Z.[Zhen], Jia, Y.H.[Yong-Hong], Zhang, Q.[Qian],
Joint image registration and point spread function estimation for the super-resolution of satellite images,
SP:IC(58), No. 1, 2017, pp. 199-211.
Elsevier DOI 1710
Super-resolution BibRef

Haris, M., Watanabe, T., Fan, L., Widyanto, M.R., Nobuhara, H.,
Superresolution for UAV Images via Adaptive Multiple Sparse Representation and Its Application to 3-D Reconstruction,
GeoRS(55), No. 7, July 2017, pp. 4047-4058.
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) BibRef

Zhang, T.[Ting], Du, Y.[Yi], Lu, F.F.[Fang-Fang],
Super-Resolution Reconstruction of Remote Sensing Images Using Multiple-Point Statistics and Isometric Mapping,
RS(9), No. 7, 2017, pp. xx-yy.
DOI Link 1708
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Wang, P.[Peng], Wang, L.G.[Li-Guo], Wu, Y.[Yiquan], Leung, H.[Henry],
Utilizing Pansharpening Technique to Produce Sub-Pixel Resolution Thematic Map from Coarse Remote Sensing Image,
RS(10), No. 6, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Wang, P.[Peng], Zhang, G.[Gong], Hao, S.Y.[Si-Yuan], Wang, L.G.[Li-Guo],
Improving Remote Sensing Image Super-Resolution Mapping Based on the Spatial Attraction Model by Utilizing the Pansharpening Technique,
RS(11), No. 3, 2019, pp. xx-yy.
DOI Link 1902
BibRef

Chang, Y.P.[Yun-Peng], Luo, B.[Bin],
Bidirectional Convolutional LSTM Neural Network for Remote Sensing Image Super-Resolution,
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 Networks,
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], Xi, Z.H.[Zhi-Hong], Gao, L.R.[Lian-Ru], Zhang, B.[Bing],
Remote Sensing Image Super-Resolution Using Novel Dense-Sampling Networks,
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

Chopade, P.B.[Pravin B.], Patil, P.M.[Pradeep M.],
Multiframe image superresolution based on cepstral analysis,
SIViP(13), No. 1, February 2019, pp. 199-207.
WWW Link. 1901
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Li, X.W.[Xiao-Wei], Li, L.[Lei], Wang, Q.H.[Qiong-Hua],
Wavelet-based iterative perfect reconstruction in computational integral imaging,
JOSA-A(35), No. 7, July 2018, pp. 1212-1220.
DOI Link 1912
Image processing, Image reconstruction techniques, Computational imaging, Image processing, Image quality, Signal processing BibRef

Gong, Y.F.[Yuan-Fu], Liao, P.[Puyun], Zhang, X.D.[Xiao-Dong], Zhang, L.F.[Li-Fei], Chen, G.Z.[Guan-Zhou], Zhu, K.[Kun], Tan, X.L.[Xiao-Liang], Lv, Z.Y.[Zhi-Yong],
Enlighten-GAN for Super Resolution Reconstruction in Mid-Resolution Remote Sensing Images,
RS(13), No. 6, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Zhang, D.Y.[Dong-Yang], Shao, J.[Jie], Li, X.Y.[Xin-Yao], 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], Liu, S.G.[Shi-Gang],
Pre-training of gated convolution neural network for remote sensing image super-resolution,
IET-IPR(15), No. 5, 2021, pp. 1179-1188.
DOI Link 2106
BibRef

Zhao, M.H.[Ming-Hua], Ning, J.W.[Jia-Wei], Hu, J.[Jing], 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], Zou, N.[Nan], Wang, C.[Chao], Xie, Y.Q.[Ya-Qin], Xie, Y.[Yong], Xu, D.D.[Dong-Dong],
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 Reconstruction of Remote Sensing Images,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Huang, B.[Bo], Guo, Z.M.[Zhi-Ming], Wu, L.[Liaoni], 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], Xu, M.Z.[Miao-Zhong], Zhong, Y.F.[Yan-Fei], Cheng, Y.F.[Yu-Feng],
GAN-Assisted Two-Stream Neural Network for High-Resolution Remote Sensing Image Classification,
RS(9), No. 12, 2017, pp. xx-yy.
DOI Link 1802
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Hu, F.[Fan], Xia, G.S.[Gui-Song], Hu, J.W.[Jing-Wen], Zhang, L.P.[Liang-Pei],
Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery,
RS(7), No. 11, 2015, pp. 14680.
DOI Link 1512
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Tao, Y.T.[Yi-Ting], Xu, M.Z.[Miao-Zhong], Zhang, F.[Fan], Du, B.[Bo], Zhang, L.P.[Liang-Pei],
Unsupervised-Restricted Deconvolutional Neural Network for Very High Resolution Remote-Sensing Image Classification,
GeoRS(55), No. 12, December 2017, pp. 6805-6823.
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], Lu, Z.Y.[Zhong-Yuan], Zhong, Y.F.[Yan-Fei],
Attention-Mechanism-Containing Neural Networks for High-Resolution Remote Sensing Image Classification,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811
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Ma, Y.C.[Yun-Chuan], Lv, P.Y.[Peng-Yuan], Liu, H.[Hao], Sun, X.H.[Xue-Hong], Zhong, Y.F.[Yan-Fei],
Remote Sensing Image Super-Resolution Based on Dense Channel Attention Network,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108
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Yang, X.[Xin], Xie, T.X.[Tang-Xin], Guo, Y.Q.[Ying-Qing], Zhou, D.[Dake],
Remote sensing image super-resolution based on convolutional blind denoising adaptive dense connection,
IET-IPR(15), No. 11, 2021, pp. 2508-2520.
DOI Link 2108
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Zhang, L.Z.[Li-Ze], Lu, W.[Wen], Huang, Y.F.[Yuan-Fei], Sun, X.P.[Xiao-Peng], Zhang, H.Y.[Hong-Yi],
Unpaired Remote Sensing Image Super-Resolution with Multi-Stage Aggregation Networks,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
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Cai, J.J.[Jia-Jun], Huang, B.[Bo],
Super-Resolution-Guided Progressive Pansharpening Based on a Deep Convolutional Neural Network,
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) BibRef

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.
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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], Qi, K.L.[Kun-Lun], Liu, W.X.[Wen-Xuan],
A hierarchical self-attention augmented Laplacian pyramid expanding network for change detection in high-resolution remote sensing images,
PandRS(182), 2021, pp. 52-66.
Elsevier DOI 2112
High-resolution remote sensing image, Change detection, Convolutional neural network, Self-attention, Laplacian pyramid BibRef

Zhang, N.[Ning], Wang, Y.C.[Yong-Cheng], Zhang, X.[Xin], Xu, D.D.[Dong-Dong], Wang, X.D.[Xiao-Dong], Ben, G.[Guangli], Zhao, Z.[Zhikang], 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], Guo, Z.K.[Zhao-Kang], Feng, Z.L.[Zi-Liang],
Semi-supervised image super-resolution with attention CycleGAN,
IET-IPR(16), No. 4, 2022, pp. 1181-1193.
DOI Link 2203
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Yue, X.C.[Xiu-Chao], Chen, X.X.[Xiao-Xuan], Zhang, W.[Wanxu], 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
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Guo, M.Q.[Ming-Qiang], Zhang, Z.[Zeyuan], Liu, H.[Heng], Huang, Y.[Ying],
NDSRGAN: A Novel Dense Generative Adversarial Network for Real Aerial Imagery Super-Resolution Reconstruction,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link 2205
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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
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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
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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
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Wang, X.[Xuan], Yi, J.[Jinglei], 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.[Haigen],
A Review of Image Super-Resolution Approaches Based on Deep Learning and Applications in Remote Sensing,
RS(14), No. 21, 2022, pp. xx-yy.
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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 Images,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link 2204
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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
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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
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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
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An, T.[Tai], Huo, C.L.[Chun-Lei], Xiang, S.M.[Shi-Ming], Pan, C.H.[Chun-Hong],
Combining Discrete and Continuous Representation: Scale-Arbitrary Super-Resolution for Satellite Images,
RS(15), No. 7, 2023, pp. 1827.
DOI Link 2304
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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 Super-Resolution,
RS(15), No. 9, 2023, pp. xx-yy.
DOI Link 2305
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Qiu, Z.H.[Zhong-Hang], Shen, H.F.[Huan-Feng], 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], Kim, J.[Jongmin], Lee, K.[Kyungdo], Lim, J.[Joongbin], Jang, K.[Keunchang], Chun, J.[Junghwa], Kim, K.M.[Kyoung-Min], Houborg, R.[Rasmus],
Super resolution of historic Landsat imagery using a dual generative adversarial network (GAN) model with CubeSat constellation imagery for spatially enhanced long-term vegetation monitoring,
PandRS(200), 2023, pp. 1-23.
Elsevier DOI 2306
CubeSat, Deep learning, Generative adversarial network, Landsat, Super-resolution, Vegetation index BibRef

Zheng, P.C.[Peng-Cheng], Jiang, J.A.[Jian-An], Zhang, Y.[Yan], Zeng, C.X.[Cheng-Xiao], Qin, C.C.[Chuan-Chuan], Li, Z.H.[Zheng-Hao],
CGC-Net: A Context-Guided Constrained Network for Remote-Sensing Image Super Resolution,
RS(15), No. 12, 2023, pp. xx-yy.
DOI Link 2307
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Chang, Y.[Yali], Chen, G.[Gang], Chen, J.[Jifa],
Pixel-Wise Attention Residual Network for Super-Resolution of Optical Remote Sensing Images,
RS(15), No. 12, 2023, pp. xx-yy.
DOI Link 2307
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Han, L.T.[Lin-Tao], Zhao, Y.C.[Yu-Chen], Lv, H.Y.[Heng-Yi], Zhang, Y.[Yisa], Liu, H.L.[Hai-Long], Bi, G.L.[Guo-Ling], Han, Q.[Qing],
Enhancing Remote Sensing Image Super-Resolution with Efficient Hybrid Conditional Diffusion Model,
RS(15), No. 13, 2023, pp. 3452.
DOI Link 2307
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Shang, J.R.[Jian-Run], Gao, M.L.[Ming-Liang], Li, Q.[Qilei], Pan, J.F.[Jin-Feng], Zou, G.F.[Guo-Feng], Jeon, G.G.[Gwang-Gil],
Hybrid-Scale Hierarchical Transformer for Remote Sensing Image Super-Resolution,
RS(15), No. 13, 2023, pp. 3442.
DOI Link 2307
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Chung, M.Y.[Mink-Yung], Jung, M.Y.[Min-Young], Kim, Y.[Yongil],
Enhancing Remote Sensing Image Super-Resolution Guided by Bicubic-Downsampled Low-Resolution Image,
RS(15), No. 13, 2023, pp. 3309.
DOI Link 2307
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Yue, X.H.[Xiao-Han], Liu, D.F.[Dan-Feng], Wang, L.G.[Li-Guo], 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], Yang, X.[Xiubin], Tang, X.Y.[Xing-Yu], Xu, T.T.[Ting-Ting], 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
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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], Zhang, L.P.[Liang-Pei],
TTST: A Top-k Token Selective Transformer for Remote Sensing Image Super-Resolution,
IP(33), 2024, pp. 738-752.
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], Yu, Q.[Qing], Li, X.[Xin], Zeng, H.[Hui], Zhang, H.L.[Hai-Long], Gao, H.M.[Hong-Min],
A CBAM-GAN-based method for super-resolution reconstruction of remote sensing image,
IET-IPR(18), No. 2, 2024, pp. 548-560.
DOI Link 2402
computer vision, image resolution, remote sensing BibRef

W?sala, J.[Julia], Marselis, S.[Suzanne], Arp, L.[Laurens], Hoos, H.[Holger], Longépé, N.[Nicolas], Baratchi, M.[Mitra],
AutoSR4EO: An AutoML Approach to Super-Resolution for Earth Observation Images,
RS(16), No. 3, 2024, pp. 443.
DOI Link 2402
BibRef

Zhang, W.J.[Wen-Jian], Tan, Z.[Zheng], Lv, Q.[Qunbo], Li, J.[Jiaao], 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 2404
BibRef

Hu, W.[Wenyi], Ju, L.[Lei], Du, Y.[Yujia], Li, Y.X.[Yu-Xia],
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


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, Pattern recognition, 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 .


Last update:May 29, 2024 at 17:34:46