Rane, S.D.,
Sapiro, G.,
Bertalmio, M.,
Structure and texture filling-in of missing image blocks in wireless
transmission and compression applications,
IP(12), No. 3, March 2003, pp. 296-303.
IEEE DOI
0301
BibRef
Earlier:
ICIP02(I: 317-320).
IEEE DOI
0210
BibRef
Bertalmio, M.,
Vese, L.A.,
Sapiro, G.,
Osher, S.J.,
Simultaneous structure and texture image inpainting,
IP(12), No. 8, August 2003, pp. 882-889.
IEEE DOI
0308
BibRef
Earlier:
CVPR03(II: 707-712).
IEEE DOI
0307
BibRef
And:
Image filling-in in a decomposition space,
ICIP03(I: 853-856).
IEEE DOI
0312
See also Video Inpainting Under Constrained Camera Motion.
BibRef
Bertalmio, M.,
Sapiro, G.,
Caselles, V., and
Ballester, C.,
Image inpainting,
SIGGraph-2000(417-424).
BibRef
0001
Yatziv, L.,
Sapiro, G.,
Levoy, M.,
Lightfield completion,
ICIP04(III: 1787-1790).
IEEE DOI
0505
Fill in gaps due to occlusions.
BibRef
Bertalmio, M.,
Strong-Continuation, Contrast-Invariant Inpainting With a Third-Order
Optimal PDE,
IP(15), No. 7, July 2006, pp. 1934-1938.
IEEE DOI
0606
BibRef
Ballester, C.,
Caselles, V.,
Verdera, J.,
A variational model for disocclusion,
ICIP03(III: 677-680).
IEEE DOI
0312
BibRef
Lin, H.J.[Hwei-Jen],
Wang, C.W.[Chun-Wei],
Hsieh, Y.C.[Yuan-Chun],
Kao, Y.T.[Yang-Ta],
Image restoration with broken curve prediction,
IJCVR(1), No. 1, 2009, pp. 110-120.
DOI Link
0911
Detect missing edges for inpainting.
BibRef
Yang, S.B.[Shiueng-Bien],
Liang, T.W.[Ting-Wen],
Image Restoration Based on Smooth Gray-level Detection and Line
Prediction Method for Large Missing Regions,
IJIG(12), No. 2, April 2012, pp. 1250013.
DOI Link
1205
BibRef
Lobato, F.[Fabio],
Sales, C.[Claudomiro],
Araujo, I.[Igor],
Tadaiesky, V.[Vincent],
Dias, L.[Lilian],
Ramos, L.[Leonardo],
Santana, A.[Adamo],
Multi-objective genetic algorithm for missing data imputation,
PRL(68, Part 1), No. 1, 2015, pp. 126-131.
Elsevier DOI
1512
Missing data
BibRef
Madathil, B.[Baburaj],
George, S.N.[Sudhish N.],
A novel dictionary-based approach for missing sample recovery of
signals in manifold,
SIViP(11), No. 2, February 2017, pp. 283-290.
Springer DOI
1702
BibRef
Fortunati, S.[Stefano],
Gini, F.[Fulvio],
Greco, M.S.[Maria S.],
Richmond, C.D.[Christ D.],
Performance Bounds for Parameter Estimation under Misspecified
Models: Fundamental Findings and Applications,
SPMag(34), No. 6, November 2017, pp. 142-157.
IEEE DOI
1712
Bayes methods, Biological system modeling, Data models,
Estimation theory, Maximum likelihood estimation, Signal processing algorithms.
Missing data.
BibRef
Gerber, F.,
de Jong, R.,
Schaepman, M.E.,
Schaepman-Strub, G.,
Furrer, R.,
Predicting Missing Values in Spatio-Temporal Remote Sensing Data,
GeoRS(56), No. 5, May 2018, pp. 2841-2853.
IEEE DOI
1805
Indexes, MODIS, Open source software, Prediction methods,
Remote sensing, Uncertainty, Alaska,
uncertainty
BibRef
Dai, J.,
Hu, H.,
Hu, Q.,
Huang, W.,
Zheng, N.,
Liu, L.,
Locally Linear Approximation Approach for Incomplete Data,
Cyber(48), No. 6, June 2018, pp. 1720-1732.
IEEE DOI
1805
Approximation algorithms, Computer science, Correlation,
Image reconstruction, Linear approximation,
matrix completion
BibRef
Zhang, Q.,
Yuan, Q.,
Zeng, C.,
Li, X.,
Wei, Y.,
Missing Data Reconstruction in Remote Sensing Image With a Unified
Spatial-Temporal-Spectral Deep Convolutional Neural Network,
GeoRS(56), No. 8, August 2018, pp. 4274-4288.
IEEE DOI
1808
image reconstruction, neural nets, remote sensing,
unified spatial-temporal-spectral framework, unified deep CNN,
spatial-temporal-spectral
BibRef
Liu, X.M.[Xiao-Ming],
Wang, M.[Menghua],
Gap Filling of Missing Data for VIIRS Global Ocean Color Products
Using the DINEOF Method,
GeoRS(56), No. 8, August 2018, pp. 4464-4476.
IEEE DOI
1808
atmospheric techniques, infrared imaging, interpolation,
oceanographic regions, oceanographic techniques, radiometers,
Visible Infrared Imaging Radiometer Suite (VIIRS)
BibRef
Liu, X.M.[Xiao-Ming],
Wang, M.[Menghua],
Filling the Gaps of Missing Data in the Merged VIIRS SNPP/NOAA-20
Ocean Color Product Using the DINEOF Method,
RS(11), No. 2, 2019, pp. xx-yy.
DOI Link
1902
BibRef
Holloway, J.[Jacinta],
Helmstedt, K.J.[Kate J.],
Mengersen, K.[Kerrie],
Schmidt, M.[Michael],
A Decision Tree Approach for Spatially Interpolating Missing Land
Cover Data and Classifying Satellite Images,
RS(11), No. 15, 2019, pp. xx-yy.
DOI Link
1908
BibRef
Liu, Y.,
Long, Z.,
Huang, H.,
Zhu, C.,
Low CP Rank and Tucker Rank Tensor Completion for Estimating Missing
Components in Image Data,
CirSysVideo(30), No. 4, April 2020, pp. 944-954.
IEEE DOI
2004
Optimization, Matrix decomposition, Data structures,
Convex functions, Numerical models, Minimization methods,
Tucker decomposition
BibRef
Chai, X.,
Tang, G.,
Wang, S.,
Peng, R.,
Chen, W.,
Li, J.,
Deep Learning for Regularly Missing Data Reconstruction,
GeoRS(58), No. 6, June 2020, pp. 4406-4423.
IEEE DOI
2005
Convolutional neural networks (CNNs), deep learning (DL),
missing data reconstruction
BibRef
Chi, Y.F.[Yu-Feng],
Wu, Z.F.[Zhi-Feng],
Liao, K.[Kuo],
Ren, Y.[Yin],
Handling Missing Data in Large-Scale MODIS AOD Products Using a
Two-Step Model,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link
2011
BibRef
Dong, B.[Bin],
Ju, H.C.[Hao-Cheng],
Lu, Y.P.[Yi-Ping],
Shi, Z.Q.[Zuo-Qiang],
CURE: Curvature Regularization for Missing Data Recovery,
SIIMS(13), No. 4, 2020, pp. 2169-2188.
DOI Link
2012
BibRef
Xu, H.W.[Han-Wen],
Tang, X.M.[Xin-Ming],
Ai, B.[Bo],
Gao, X.M.[Xiao-Ming],
Yang, F.L.[Fan-Lin],
Wen, Z.[Zhen],
Missing data reconstruction in VHR images based on progressive
structure prediction and texture generation,
PandRS(171), 2021, pp. 266-277.
Elsevier DOI
2012
Missing data reconstruction, VHR images,
Progressive structure prediction, Texture generation, Deep learning
BibRef
Case, N.[Nicola],
Vitti, A.[Alfonso],
Reconstruction of Multi-Temporal Satellite Imagery by Coupling
Variational Segmentation and Radiometric Analysis,
IJGI(10), No. 1, 2021, pp. xx-yy.
DOI Link
2101
Correcting for missing data from sensors.
BibRef
Teodoro, A.M.,
Bioucas-Dias, J.M.,
Figueiredo, M.A.T.,
Block-Gaussian-Mixture Priors for Hyperspectral Denoising and
Inpainting,
GeoRS(59), No. 3, March 2021, pp. 2478-2486.
IEEE DOI
2103
Noise reduction, Noise measurement, Gaussian mixture model,
Feature extraction, Data mining, Transforms,
image denoising
BibRef
Zdunek, R.[Rafal],
Sadowski, T.[Tomasz],
Image completion with approximate convex hull tensor decomposition,
SP:IC(95), 2021, pp. 116276.
Elsevier DOI
2106
Image completion, Convex-hull algorithm,
Near-separable tensor decompositions, Non-negative tensor factorization
BibRef
Barmherzig, D.A.[David A.],
Barnett, A.H.[Alex H.],
Epstein, C.L.[Charles L.],
Greengard, L.F.[Leslie F.],
Magland, J.F.[Jeremy F.],
Rachh, M.[Manas],
Recovering Missing Data in Coherent Diffraction Imaging,
SIIMS(14), No. 2, 2021, pp. 620-644.
DOI Link
2107
BibRef
Xie, M.H.[Ming-Hong],
Wang, J.X.[Jia-Xin],
Zhang, Y.[Yafei],
A unified framework for damaged image fusion and completion based on
low-rank and sparse decomposition,
SP:IC(98), 2021, pp. 116400.
Elsevier DOI
2109
Image fusion, Image completion, Image decomposition,
Low-rank and sparse representation, Dictionary learning
BibRef
Wan, D.M.[Dao-Ming],
Razavi-Far, R.[Roozbeh],
Saif, M.[Mehrdad],
Mozafari, N.[Niloofar],
COLI: Collaborative clustering missing data imputation,
PRL(152), 2021, pp. 420-427.
Elsevier DOI
2112
Missing data imputation, Collaborative clustering, Data amputation
BibRef
An, W.N.[Wei-Ning],
Zhang, X.[Xinqi],
Wu, H.[Hang],
Zhang, W.C.[Wen-Chang],
Du, Y.[Yaohua],
Sun, J.G.[Jing-Gong],
LPIN: A Lightweight Progressive Inpainting Network for Improving the
Robustness of Remote Sensing Images Scene Classification,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link
2201
BibRef
Tang, Z.P.[Zhi-Peng],
Amatulli, G.[Giuseppe],
Pellikka, P.K.E.[Petri K. E.],
Heiskanen, J.[Janne],
Spectral Temporal Information for Missing Data Reconstruction
(STIMDR) of Landsat Reflectance Time Series,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link
2201
BibRef
Lee, W.[Woojin],
Lee, S.[Sungyoon],
Byun, J.Y.[Jun-Young],
Kim, H.[Hoki],
Lee, J.W.[Jae-Wook],
Variational cycle-consistent imputation adversarial networks for
general missing patterns,
PR(129), 2022, pp. 108720.
Elsevier DOI
2206
Imputation, Missing data, Cycle-consistent
BibRef
Lasko, K.[Kristofer],
Gap Filling Cloudy Sentinel-2 NDVI and NDWI Pixels with
Multi-Frequency Denoised C-Band and L-Band Synthetic Aperture Radar
(SAR), Texture, and Shallow Learning Techniques,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link
2209
BibRef
Liang, W.[Wei],
Li, Y.H.[Yu-Hui],
Xie, K.[Kun],
Zhang, D.F.[Da-Fang],
Li, K.C.[Kuan-Ching],
Souri, A.[Alireza],
Li, K.Q.[Ke-Qin],
Spatial-Temporal Aware Inductive Graph Neural Network for C-ITS Data
Recovery,
ITS(24), No. 8, August 2023, pp. 8431-8442.
IEEE DOI
2308
Sensors, Data models, Task analysis, Adaptation models,
Numerical models, Intelligent sensors, Graph neural networks,
spatial-temporal
BibRef
Liu, J.[Jiang],
Pasumarthi, S.[Srivathsa],
Duffy, B.[Ben],
Gong, E.[Enhao],
Datta, K.[Keshav],
Zaharchuk, G.[Greg],
One Model to Synthesize Them All: Multi-Contrast Multi-Scale
Transformer for Missing Data Imputation,
MedImg(42), No. 9, September 2023, pp. 2577-2591.
IEEE DOI
2310
BibRef
Özbey, M.[Muzaffer],
Dalmaz, O.[Onat],
Dar, S.U.H.[Salman U. H.],
Bedel, H.A.[Hasan A.],
Özturk, S.[Saban],
Güngör, A.[Alper],
Çukur, T.[Tolga],
Unsupervised Medical Image Translation With Adversarial Diffusion
Models,
MedImg(42), No. 12, December 2023, pp. 3524-3539.
IEEE DOI
2312
adversarial diffusion modeling to generate missing images.
BibRef
Yu, X.Y.[Xiao-Yu],
Pan, J.[Jun],
Xu, J.[Jiangong],
Wang, M.[Mi],
Missing information reconstruction integrating isophote constraint
and color-structure control for remote sensing data,
PandRS(208), 2024, pp. 261-278.
Elsevier DOI Code:
WWW Link.
2402
Information reconstruction, Isophote constraint,
Color-structure control, Remote sensing imagery
BibRef
Li, Y.[Yue],
Liu, Q.[Qiang],
Chen, S.[Shuang],
Zhang, X.T.[Xiao-Tong],
An Improved Gap-Filling Method for Reconstructing Dense Time-Series
Images from LANDSAT 7 SLC-Off Data,
RS(16), No. 12, 2024, pp. 2064.
DOI Link
2406
BibRef
Ali, T.F.[Thaer F.],
Woodley, A.[Alan],
Using Environmental Context to Synthesis Missing Pixels,
DICTA20(1-7)
IEEE DOI
2201
Satellites, Data analysis, Preforms, Digital images,
Clustering algorithms, Prediction algorithms, IGTMPP
BibRef
Shin, C.J.[Cha-Jin],
Kim, T.[Taeoh],
Lee, S.J.[Sang-Jin],
Leey, S.Y.[Sang-Youn],
Test-Time Adaptation for Out-Of-Distributed Image Inpainting,
ICIP21(2009-2013)
IEEE DOI
2201
Training, Adaptation models, Image processing, Image Inpainting,
Internal Learning, Test-time Adaptation
BibRef
Becker, S.[Stefan],
Hug, R.[Ronny],
Huebner, W.[Wolfgang],
Arens, M.[Michael],
Morris, B.T.[Brendan Tran],
MissFormer: (In-)Attention-Based Handling of Missing Observations for
Trajectory Filtering and Prediction,
ISVC21(I:521-533).
Springer DOI
2112
BibRef
Yoon, S.,
Sull, S.,
GAMIN: Generative Adversarial Multiple Imputation Network for Highly
Missing Data,
CVPR20(8453-8461)
IEEE DOI
2008
Generators, Generative adversarial networks,
Stochastic processes, Measurement, Unsupervised learning, Prediction methods
BibRef
Lee, D.W.[Dong-Wook],
Kim, J.Y.[Jun-Young],
Moon, W.J.[Won-Jin],
Ye, J.C.[Jong Chul],
CollaGAN: Collaborative GAN for Missing Image Data Imputation,
CVPR19(2482-2491).
IEEE DOI
2002
BibRef
Chen, X.,
An Improved Self-Representation Approach for Missing Value Imputation,
ICPR18(1450-1455)
IEEE DOI
1812
Roads, Sensors, Optimization, Estimation, Data analysis,
Matrix decomposition, Indexes, graph regularization, missing values
BibRef
Yokota, T.[Tatsuya],
Erem, B.[Burak],
Guler, S.[Seyhmus],
Warfield, S.K.[Simon K.],
Hontani, H.[Hidekata],
Missing Slice Recovery for Tensors Using a Low-Rank Model in Embedded
Space,
CVPR18(8251-8259)
IEEE DOI
1812
Delays, Transforms, Minimization, Standards, Optimization, Matrix decomposition
BibRef
Tran, L.[Luan],
Liu, X.M.[Xiao-Ming],
Zhou, J.Y.[Jia-Yu],
Jin, R.[Rong],
Missing Modalities Imputation via Cascaded Residual Autoencoder,
CVPR17(4971-4980)
IEEE DOI
1711
Some of the multi-modal data is missing.
Data models, Genetic algorithms, Matrix decomposition,
Object recognition, Sensors, Training
BibRef
Xu, Z.,
Li, Y.,
Huang, J.,
Accelerated sparse optimization for missing data completion,
ICPR16(1267-1272)
IEEE DOI
1705
Closed-form solutions, Convergence,
Matrix decomposition, Minimization, TV, Visualization
BibRef
Campos, S.[Sergio],
Pizarro, L.[Luis],
Valle, C.[Carlos],
Gray, K.R.[Katherine R.],
Rueckert, D.[Daniel],
Allende, H.[Héctor],
Evaluating Imputation Techniques for Missing Data in ADNI:
A Patient Classification Study,
CIARP15(3-10).
Springer DOI
1511
BibRef
Ryazanov, V.[Vladimir],
Some Imputation Algorithms for Restoration of Missing Data,
CIARP11(372-379).
Springer DOI
1111
BibRef
Herlin, I.[Isabelle],
Béréziat, D.[Dominique],
Mercier, N.[Nicolas],
Recovering Missing Data on Satellite Images,
SCIA11(697-707).
Springer DOI
1105
BibRef
Corrigan, D.,
Harte, N.,
Kokaram, A.,
Pathological Motion Detection for Robust Missing Data Treatment in
Degraded Archived Media,
ICIP06(621-624).
IEEE DOI
0610
BibRef
Gan, X.C.[Xiang-Chao],
Liew, A.W.C.[Alan Wee-Chung],
Yan, H.[Hong],
Microarray Missing Data Imputation based on a Set Theoretic Framework
and Biological Constraints,
ICPR06(III: 842-845).
IEEE DOI
0609
BibRef
Han, F.[Feng],
Zhu, S.C.[Song-Chun],
Bayesian reconstruction of 3D shapes and scenes from a single image,
HLK03(12-20).
IEEE Abstract.
0402
Fill in missing parts of image from model.
BibRef
Chapter on 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing continues in
Inpainting Face Images, Facial Images .