19.4.3.22 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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Lv, Z.[Zhen], Jia, Y.H.[Yong-Hong], Zhang, Q.[Qian],
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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,
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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],
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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,
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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,
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Chang, Y.P.[Yun-Peng], Luo, B.[Bin],
Bidirectional Convolutional LSTM Neural Network for Remote Sensing Image Super-Resolution,
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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,
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DOI Link 1912
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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

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Enlighten-GAN for Super Resolution Reconstruction in Mid-Resolution Remote Sensing Images,
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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],
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DOI Link 2106
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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
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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,
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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,
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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,
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Tao, Y.T.[Yi-Ting], Xu, M.Z.[Miao-Zhong], Zhong, Y.F.[Yan-Fei], Cheng, Y.F.[Yu-Feng],
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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], Lu, Z.Y.[Zhong-Yuan], Zhong, Y.F.[Yan-Fei],
Attention-Mechanism-Containing Neural Networks for High-Resolution Remote Sensing Image Classification,
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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,
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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,
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He, Z.[Zhi], He, D.[Dan],
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GeoRS(59), No. 10, October 2021, pp. 8812-8825.
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Cheng, H.Q.[Hong-Quan], Wu, H.Y.[Hua-Yi], Zheng, J.[Jie], Qi, K.L.[Kun-Lun], Liu, W.X.[Wen-Xuan],
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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],
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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],
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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.
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Cai, Y.X.[Yu-Xi], Gao, G.[Guxue], Jia, Z.H.[Zhen-Hong], Lai, H.C.[Hui-Cheng],
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RS(14), No. 9, 2022, pp. xx-yy.
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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.
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Zabalza, M.[Maialen], Bernardini, A.[Angela],
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RS(14), No. 12, 2022, pp. xx-yy.
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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],
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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],
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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],
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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.
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Karwowska, K.[Kinga], Wierzbicki, D.[Damian],
Improving Spatial Resolution of Satellite Imagery Using Generative Adversarial Networks and Window Functions,
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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,
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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,
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An, T.[Tai], Huo, C.L.[Chun-Lei], Xiang, S.M.[Shi-Ming], Pan, C.H.[Chun-Hong],
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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,
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Qiu, Z.H.[Zhong-Hang], Shen, H.F.[Huan-Feng], Yue, L.W.[Lin-Wei], Zheng, G.Z.[Gui-Zhou],
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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],
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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,
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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,
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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],
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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],
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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,
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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,
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Wang, C.Y.[Chun-Yang], Zhang, X.[Xian], Yang, W.[Wei], Wang, G.[Gaige], Zhao, Z.Z.[Zong-Ze], Liu, X.[Xuan], Lu, B.[Bibo],
Landsat-8 to Sentinel-2 Satellite Imagery Super-Resolution-Based Multiscale Dilated Transformer Generative Adversarial Networks,
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DOI Link 2311
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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
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], 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


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:Mar 16, 2024 at 20:36:19