Scambos, T.A.,
Kvaran, G.,
Fahnestock, M.A.,
Improving AVHRR Resolution Through Data Cumulation for Mapping Polar
Ice Sheets,
RSE(69), No. 1, 1999, pp. 56-66.
9910
BibRef
del Carmen Valdes, M.[Maria],
Inamura, M.[Minoru],
Spatial Resolution Improvement of Remotely Sensed Images by a Fully
Interconnected Neural Network Approach,
GeoRS(38), No. 5, September 2000, pp. 2426-2429.
IEEE Top Reference.
0010
BibRef
Tatem, A.J.,
Lewis, H.G.,
Atkinson, P.M.,
Nixon, M.S.,
Super-resolution target identification from remotely sensed images
using a Hopfield neural network,
GeoRS(39), No. 4, April 2001, pp. 781-796.
IEEE Top Reference.
0105
See also Predicting Missing Field Boundaries to Increase Per-Field Classification Accuracy.
BibRef
Wang, Q.M.[Qun-Ming],
Shi, W.Z.[Wen-Zhong],
Atkinson, P.M.[Peter M.],
Sub-pixel mapping of remote sensing images based on radial basis
function interpolation,
PandRS(92), No. 1, 2014, pp. 1-15.
Elsevier DOI
1407
Downscaling
BibRef
Wang, Q.M.[Qun-Ming],
Atkinson, P.M.,
Shi, W.Z.[Wen-Zhong],
Indicator Cokriging-Based Subpixel Mapping Without Prior Spatial
Structure Information,
GeoRS(53), No. 1, January 2015, pp. 309-323.
IEEE DOI
1410
deconvolution
BibRef
Wang, Q.M.[Qun-Ming],
Shi, W.Z.[Wen-Zhong],
Atkinson, P.M.,
Spatiotemporal Subpixel Mapping of Time-Series Images,
GeoRS(54), No. 9, September 2016, pp. 5397-5411.
IEEE DOI
1609
feature extraction
See also Allocating Classes for Soft-Then-Hard Subpixel Mapping Algorithms in Units of Class.
BibRef
Wang, Q.M.[Qun-Ming],
Atkinson, P.M.,
Shi, W.Z.[Wen-Zhong],
Fast Subpixel Mapping Algorithms for Subpixel Resolution Change
Detection,
GeoRS(53), No. 4, April 2015, pp. 1692-1706.
IEEE DOI
1502
geophysical image processing
See also Area-to-point regression kriging for pan-sharpening.
BibRef
Atkinson, P.M.[Peter M.],
Sub-pixel Target Mapping from Soft-classified, Remotely Sensed Imagery,
PhEngRS(71), No. 7, July 2005, pp. 839-846.
A simple and efficient pixel-swapping algorithm for increasing the
spatial resolution of land-cover classification from remotely sensed
imagery.
WWW Link.
0509
BibRef
Tatem, A.J.,
Lewis, H.G.,
Atkinson, P.M.,
Nixon, M.S.,
Super-resolution land cover pattern prediction using a Hopfield neural
network,
RSE(79), No. 1, January 2002, pp. 1-14.
HTML Version.
0201
BibRef
Atkinson, P.M.,
Pardo-Iguzquiza, E.,
Chica-Olmo, M.,
Downscaling Cokriging for Super-Resolution Mapping of Continua in
Remotely Sensed Images,
GeoRS(46), No. 2, February 2008, pp. 573-580.
IEEE DOI
0801
BibRef
Nguyen, M.Q.,
Atkinson, P.M.,
Lewis, H.G.,
Superresolution Mapping Using a Hopfield Neural Network With Fused
Images,
GeoRS(44), No. 3, March 2006, pp. 736-749.
IEEE DOI
0604
BibRef
Fryer, J.G.[John G.],
McIntosh, K.[Kerry],
Enhancement of Image Resolution in Digital Photogrammetry,
PhEngRS(67), No. 6, June 2001, pp. 741-750.
WWW Link.
0108
Rigorously combine multiple low-resolution digital
images into a higher resolution composite, apply to photogrammetric
DEM generation.
BibRef
Galbraith, A.E.,
Theiler, J.,
Thome, K.J.,
Ziolkowski, R.W.,
Resolution Enhancement of Multilook Imagery for the Multispectral
Thermal Imager,
GeoRS(43), No. 9, September 2005, pp. 1964-1977.
IEEE DOI
0509
BibRef
Long, D.G.,
Spencer, M.W.,
Njoku, E.G.,
Spatial Resolution and Processing Tradeoffs for HYDROS:
Application of Reconstruction and Resolution Enhancement Techniques,
GeoRS(43), No. 1, January 2005, pp. 3-12.
IEEE Abstract.
0501
BibRef
Akgun, T.,
Altunbasak, Y.,
Mersereau, R.M.,
Super-Resolution Reconstruction of Hyperspectral Images,
IP(14), No. 11, November 2005, pp. 1860-1875.
IEEE DOI
0510
BibRef
Merino, M.T.[Maria Teresa],
Nunez, J.[Jorge],
Super-Resolution of Remotely Sensed Images With Variable-Pixel Linear
Reconstruction,
GeoRS(45), No. 5, May 2007, pp. 1446-1457.
IEEE DOI
0704
BibRef
Boucher, A.[Alexandre],
Kyriakidis, P.C.[Phaedon C.],
Integrating Fine Scale Information in Super-resolution Land-cover
Mapping,
PhEngRS(73), No. 8, August 2007, pp. 913-922.
WWW Link.
0709
Accounting for additional fine spatial resolution information can lead
to super-resolution maps with more realistic spatial patterns.
BibRef
Boucher, A.[Alexandre],
Kyriakidis, P.C.[Phaedon C.],
Cronkite-Ratcliff, C.,
Geostatistical Solutions for Super-Resolution Land Cover Mapping,
GeoRS(46), No. 1, January 2008, pp. 272-283.
IEEE DOI
0712
BibRef
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,
RS(6), No. 1, 2014, pp. 637-657.
DOI Link
1402
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
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
BibRef
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.[Lifei],
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
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
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
Video Image Restoration and Enhancement .