Yun, M.[Maojin],
Liu, L.R.[Li-Ren],
Sun, J.F.[Jian-Feng],
Liu, D.[De'an],
Transverse or axial superresolution with radial birefringent filter,
JOSA-A(21), No. 10, October 2004, pp. 1869-1874.
WWW Link.
0501
BibRef
Yun, M.[Maojin],
Liu, L.R.[Li-Ren],
Sun, J.F.[Jian-Feng],
Liu, D.[De'an],
Three-dimensional superresolution by three-zone complex pupil filters,
JOSA-A(22), No. 2, February 2005, pp. 272-277.
WWW Link.
0601
BibRef
Son, J.Y.[Jung-Young],
Chernyshov, O.[Oleksii],
Lee, C.H.[Chun-Hae],
Park, M.C.[Min-Chul],
Yano, S.[Sumio],
Depth resolution in three-dimensional images,
JOSA-A(30), No. 5, May 2013, pp. 1030-1038.
WWW Link.
1305
BibRef
Son, J.Y.[Jung-Young],
Bobrinev, V.I.[Vladimir Ivanovich],
Kim, K.T.[Kyung-Tae],
Depth resolution and displayable depth of a scene in three-dimensional
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JOSA-A(22), No. 9, September 2005, pp. 1739-1745.
WWW Link.
0601
BibRef
Liu, H.T.[Hai-Tao],
Mu, G.G.[Guo-Guang],
Lin, L.[Lie],
Fan, Z.W.[Zhong-Wei],
Optical superresolution of focused partially spatially coherent laser
beams,
JOSA-A(23), No. 6, June 2006, pp. 1301-1310.
WWW Link.
0610
BibRef
Suresh, K.V.[Kaggere V.],
Rajagopalan, A.N.[Ambasamudram N.],
Robust and computationally efficient superresolution algorithm,
JOSA-A(24), No. 4, April 2007, pp. 984-992.
WWW Link.
0801
BibRef
And:
Super-Resolution using Motion and Defocus Cues,
ICIP07(IV: 213-216).
IEEE DOI
0709
BibRef
Earlier:
Super-resolution in the presence of space-variant blur,
ICPR06(III: 770-773).
IEEE DOI
0609
See also Superresolution of License Plates in Real Traffic Videos.
BibRef
Kiran, S.S.[S. Shashi],
Suresh, K.V.,
Challenges in Sparse Image Reconstruction,
IJIG(21), No. 3, July 2021, pp. 2150026.
DOI Link
2107
BibRef
Rajagopalan, A.N.[Ambasamudram N.],
Bhavsar, A.V.[Arnav V.],
Wallhoff, F.[Frank],
Rigoll, G.[Gerhard],
Resolution Enhancement of PMD Range Maps,
DAGM08(xx-yy).
Springer DOI
0806
BibRef
Bhavsar, A.V.[Arnav V.],
Rajagopalan, A.N.[Ambasamudram N.],
Resolution enhancement for binocular stereo,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Prabhu, S.M.[Sahana M.],
Rajagopalan, A.N.[Ambasamudram N.],
Matte Super-Resolution for Compositing,
DAGM10(422-431).
Springer DOI
1009
BibRef
Yu, Y.K.[Ying Kin],
Wong, K.H.[Kin Hong],
Chang, M.M.Y.[Michael Ming Yuen],
Or, S.H.[Siu Hang],
Recursive Camera-Motion Estimation With the Trifocal Tensor,
SMC-B(36), No. 5, October 2006, pp. 1081-1090.
IEEE DOI
0609
BibRef
Yu, Y.K.[Ying Kin],
Or, S.H.[Siu Hang],
Wong, K.H.[Kin Hong],
Chang, M.M.Y.[Michael Ming Yuen],
Accurate 3-D Motion Tracking with an Application to Super-Resolution,
ICPR06(III: 730-733).
IEEE DOI
0609
BibRef
Lee, M.[Moojae],
Choi, J.J.[Jung-Ju],
Wee, Y.[Youngcheul],
Improved Orthogonal Fractal Super-Resolution Using Range Adjustment and
Domain Extension,
IEICE(E96-D), No. 8, August 2013, pp. 1890-1893.
WWW Link.
1308
BibRef
Herbort, S.[Steffen],
Gerken, B.[Britta],
Schugk, D.[Daniel],
Wöhler, C.[Christian],
3D range scan enhancement using image-based methods,
PandRS(84), No. 0, 2013, pp. 69-84.
Elsevier DOI
1309
Photometry
BibRef
Kang, M.K.[Min-Koo],
Yoon, K.J.[Kuk-Jin],
Depth-Discrepancy-Compensated Inter-Prediction With Adaptive Segment
Management for Multiview Depth Video Coding,
MultMed(16), No. 6, October 2014, pp. 1563-1573.
IEEE DOI
1410
statistical analysis
BibRef
Choi, J.[Jinwook],
Min, D.B.[Dong-Bo],
Sohn, K.H.[Kwang-Hoon],
Reliability-Based Multiview Depth Enhancement Considering Interview
Coherence,
CirSysVideo(24), No. 4, April 2014, pp. 603-616.
IEEE DOI
1405
image colour analysis
BibRef
Wang, Y.[Yanke],
Zhong, F.[Fan],
Peng, Q.S.[Qun-Sheng],
Qin, X.Y.[Xue-Ying],
Depth map enhancement based on color and depth consistency,
VC(30), No. 10, October 2014, pp. 1157-1168.
WWW Link.
1410
BibRef
Jung, S.W.[Seung-Won],
Choi, O.[Ouk],
Learning-Based Filter Selection Scheme for Depth Image Super
Resolution,
CirSysVideo(24), No. 10, October 2014, pp. 1641-1650.
IEEE DOI
1411
feature extraction
BibRef
Yoo, J.S.[Jun-Sang],
Kim, D.W.[Dong-Wook],
Lu, Y.C.[Yu-Cheng],
Jung, S.W.[Seung-Won],
RZSR: Reference-Based Zero-Shot Super-Resolution With Depth Guided
Self-Exemplars,
MultMed(25), 2023, pp. 5972-5983.
IEEE DOI
2311
BibRef
Wan, P.F.[Peng-Fei],
Cheung, G.[Gene],
Chou, P.A.,
Florencio, D.,
Zhang, C.[Cha],
Au, O.C.,
Precision Enhancement of 3-D Surfaces from Compressed Multiview Depth
Maps,
SPLetters(22), No. 10, October 2015, pp. 1676-1680.
IEEE DOI
1506
data compression
BibRef
Liu, W.[Wei],
Chen, X.G.[Xiao-Gang],
Yang, J.[Jie],
Wu, Q.A.[Qi-Ang],
Robust Color Guided Depth Map Restoration,
IP(26), No. 1, January 2017, pp. 315-327.
IEEE DOI
1612
image colour analysis
BibRef
Xie, J.[Jun],
Feris, R.S.[Rogerio Schmidt],
Yu, S.S.[Shiaw-Shian],
Sun, M.T.[Ming-Ting],
Joint Super Resolution and Denoising From a Single Depth Image,
MultMed(17), No. 9, September 2015, pp. 1525-1537.
IEEE DOI
1509
edge detection
BibRef
Xie, J.[Jun],
Feris, R.S.[Rogerio Schmidt],
Sun, M.T.[Ming-Ting],
Edge-Guided Single Depth Image Super Resolution,
IP(25), No. 1, January 2016, pp. 428-438.
IEEE DOI
1601
BibRef
Earlier:
ICIP14(3773-37777)
IEEE DOI
1502
edge detection
BibRef
Kiechle, M.[Martin],
Habigt, T.[Tim],
Hawe, S.[Simon],
Kleinsteuber, M.[Martin],
A Bimodal Co-sparse Analysis Model for Image Processing,
IJCV(114), No. 2-3, September 2015, pp. 233-247.
Springer DOI
1509
BibRef
Earlier: A1, A3, A4, Only:
A Joint Intensity and Depth Co-sparse Analysis Model for Depth Map
Super-resolution,
ICCV13(1545-1552)
IEEE DOI
1403
BibRef
Xu, Z.K.[Ze-Kai],
Wang, X.W.[Xue-Wen],
Chen, Z.X.[Zi-Xuan],
Xiong, D.P.[Dong-Ping],
Ding, M.Y.[Ming-Yue],
Hou, W.G.[Wen-Guang],
Nonlocal similarity based DEM super resolution,
PandRS(110), No. 1, 2015, pp. 48-54.
Elsevier DOI
1601
Digital elevation model
BibRef
Wang, Q.,
Li, S.,
Qin, H.,
Hao, A.,
Super-Resolution of Multi-Observed RGB-D Images Based on Nonlocal
Regression and Total Variation,
IP(25), No. 3, March 2016, pp. 1425-1440.
IEEE DOI
1602
Image edge detection
BibRef
He, H.,
Mandal, S.,
Buehler, A.,
Deán-Ben, X.L.,
Razansky, D.,
Ntziachristos, V.,
Improving Optoacoustic Image Quality via Geometric Pixel
Super-Resolution Approach,
MedImg(35), No. 3, March 2016, pp. 812-818.
IEEE DOI
1603
Detectors
BibRef
Huo, Y.,
Yang, F.,
High-dynamic range image generation from single low-dynamic range
image,
IET-IP(10), No. 3, 2016, pp. 198-205.
DOI Link
1603
image enhancement
BibRef
Sharma, R.[Rahil],
Xu, Z.W.[Ze-Wei],
Sugumaran, R.[Ramanathan],
Oliveira, S.[Suely],
Parallel Landscape Driven Data Reduction &, Spatial Interpolation
Algorithm for Big LiDAR Data,
IJGI(5), No. 6, 2016, pp. 97.
DOI Link
1608
BibRef
Yuan, Y.[Ying],
Wang, X.R.[Xiao-Rui],
Zhang, J.L.[Jian-Lei],
Wu, X.X.[Xiong-Xiong],
Zhang, Y.[Yan],
Feasibility study for super-resolution 3D integral imaging using
time-multiplexed compressive coding,
JOSA-A(33), No. 7, July 2016, pp. 1377-1384.
DOI Link
1608
Superresolution
BibRef
Jin, X.[Xin],
Xu, Y.[Yatong],
Dai, Q.H.[Qiong-Hai],
Depth dithering based on texture edge-assisted classification,
SP:IC(47), No. 1, 2016, pp. 56-71.
Elsevier DOI
1610
Depth denoising
BibRef
Mandal, S.[Srimanta],
Bhavsar, A.[Arnav],
Sao, A.K.[Anil Kumar],
Depth Map Restoration From Undersampled Data,
IP(26), No. 1, January 2017, pp. 119-134.
IEEE DOI
1612
BibRef
Earlier:
Hierarchical example-based range-image super-resolution with
edge-preservation,
ICIP14(3867-3871)
IEEE DOI
1502
image representation.
Cameras
BibRef
Kamilov, U.S.,
Boufounos, P.T.,
Motion-Adaptive Depth Superresolution,
IP(26), No. 4, April 2017, pp. 1723-1731.
IEEE DOI
1704
computer vision
BibRef
Lei, J.,
Li, L.,
Yue, H.,
Wu, F.,
Ling, N.,
Hou, C.,
Depth Map Super-Resolution Considering View Synthesis Quality,
IP(26), No. 4, April 2017, pp. 1732-1745.
IEEE DOI
1704
image resolution
BibRef
Lv, H.J.[Hui-Jin],
Zhang, Y.B.[Yong-Bing],
Li, K.[Kai],
Wang, X.Z.[Xing-Zheng],
Xuan, H.M.[Hui-Ming],
Dai, Q.H.[Qiong-Hai],
Synthesis-guided depth super resolution,
VCIP14(125-128)
IEEE DOI
1504
image enhancement
BibRef
Shabaninia, E.[Elham],
Naghsh-Nilchi, A.R.[Ahmad Reza],
Kasaei, S.[Shohreh],
High-order Markov random field for single depth image super-resolution,
IET-CV(11), No. 8, December 2017, pp. 683-690.
DOI Link
1712
BibRef
Jiang, Z.Y.[Zhong-Yu],
Hou, Y.H.[Yong-Hong],
Yue, H.J.[Huan-Jing],
Yang, J.Y.[Jing-Yu],
Hou, C.P.[Chun-Ping],
Depth Super-Resolution From RGB-D Pairs With Transform and Spatial
Domain Regularization,
IP(27), No. 5, May 2018, pp. 2587-2602.
IEEE DOI
1804
autoregressive processes, finite difference methods,
gradient methods, image colour analysis, image resolution,
sparse representation
BibRef
Yue, H.J.[Huan-Jing],
Zhou, T.[Tong],
Jiang, Z.Y.[Zhong-Yu],
Yang, J.Y.[Jing-Yu],
Hou, C.P.[Chun-Ping],
Reference guided image super-resolution via efficient dense warping
and adaptive fusion,
SP:IC(92), 2021, pp. 116062.
Elsevier DOI
2101
Super-resolution, Reference guidence, Adaptive fusion, Dense warping
BibRef
Cruz-Martinez, C.[Claudia],
Martínez-Carranza, J.[José],
Mayol-Cuevas, W.W.[Walterio W.],
Real-time enhancement of sparse 3D maps using a parallel segmentation
scheme based on superpixels,
RealTimeIP(14), No. 3, March 2018, pp. 667-683.
Springer DOI
1804
BibRef
Wang, Y.,
Zhang, J.,
Liu, Z.,
Wu, Q.,
Zhang, Z.,
Jia, Y.,
Depth Super-Resolution on RGB-D Video Sequences With Large
Displacement 3D Motion,
IP(27), No. 7, July 2018, pp. 3571-3585.
IEEE DOI
1805
Boolean functions, Data structures, Image resolution,
Motion compensation, Optical imaging, large displacement 3D motion
BibRef
Huang, X.[Xu],
Qin, R.J.[Rong-Jun],
Xiao, C.L.[Chang-Lin],
Lu, X.H.[Xiao-Hu],
Super resolution of laser range data based on image-guided fusion and
dense matching,
PandRS(144), 2018, pp. 105-118.
Elsevier DOI
1809
Super resolution, Laser range data, Image, Fusion, Matching
BibRef
Zhang, H.,
Zhang, Y.,
Wang, H.,
Ho, Y.,
Feng, S.,
WLDISR: Weighted Local Sparse Representation-Based Depth Image
Super-Resolution for 3D Video System,
IP(28), No. 2, February 2019, pp. 561-576.
IEEE DOI
1811
edge detection, image colour analysis, image reconstruction,
image representation, image resolution, image texture,
virtual view image quality
BibRef
Wen, Y.[Yang],
Sheng, B.[Bin],
Li, P.[Ping],
Lin, W.Y.[Wei-Yao],
Feng, D.D.[David Dagan],
Deep Color Guided Coarse-to-Fine Convolutional Network Cascade for
Depth Image Super-Resolution,
IP(28), No. 2, February 2019, pp. 994-1006.
IEEE DOI
1811
Image color analysis, Image edge detection, Color,
Spatial resolution, Kernel, Sensors, Depth super-resolution,
filter kernel learning
BibRef
Zhao, L.J.[Li-Jun],
Bai, H.H.[Hui-Hui],
Liang, J.[Jie],
Zeng, B.[Bing],
Wang, A.H.[An-Hong],
Zhao, Y.[Yao],
Simultaneous color-depth super-resolution with conditional generative
adversarial networks,
PR(88), 2019, pp. 356-369.
Elsevier DOI
1901
Generative adversarial networks, Super-resolution,
Image smoothing, Edge detection
BibRef
He, L.Z.[Ling-Zhi],
Zhu, H.G.[Hong-Guang],
Li, F.[Feng],
Bai, H.H.[Hui-Hui],
Cong, R.[Runmin],
Zhang, C.J.[Chun-Jie],
Lin, C.Y.[Chun-Yu],
Liu, M.[Meiqin],
Zhao, Y.[Yao],
Towards Fast and Accurate Real-World Depth Super-Resolution:
Benchmark Dataset and Baseline,
CVPR21(9225-9234)
IEEE DOI
2111
Training, Superresolution, Focusing, Estimation,
Benchmark testing, Mobile handsets
BibRef
Wang, B.,
Zou, J.,
Li, Y.,
Ju, K.,
Xiong, H.,
Zheng, Y.F.,
Sparse-to-Dense Depth Estimation in Videos via High-Dimensional
Tensor Voting,
CirSysVideo(29), No. 1, January 2019, pp. 68-79.
IEEE DOI
1901
Tensile stress, Estimation, Videos,
Motion estimation, Bidirectional control, Reliability,
bilateral filtering
BibRef
Xu, Z.[Zekai],
Chen, Z.X.[Zi-Xuan],
Yi, W.W.[Wei-Wei],
Gui, Q.L.[Qiu-Ling],
Hou, W.G.[Wen-Guang],
Ding, M.Y.[Ming-Yue],
Deep gradient prior network for DEM super-resolution: Transfer
learning from image to DEM,
PandRS(150), 2019, pp. 80-90.
Elsevier DOI
1903
Super-resolution, Digital elevation model,
Gradient reconstruction, Convolutional neural network, Transfer learning
BibRef
Guo, C.,
Li, C.,
Guo, J.,
Cong, R.,
Fu, H.,
Han, P.,
Hierarchical Features Driven Residual Learning for Depth Map
Super-Resolution,
IP(28), No. 5, May 2019, pp. 2545-2557.
IEEE DOI
1903
Color, Feature extraction, Image reconstruction,
Spatial resolution, Task analysis, Cameras,
image reconstruction
BibRef
Vosters, L.[Luc],
Varekamp, C.[Chris],
de Haan, G.[Gerard],
Overview of efficient high-quality state-of-the-art depth enhancement
methods by thorough design space exploration,
RealTimeIP(16), No. 2, April 2019, pp. 355-375.
WWW Link.
1904
BibRef
Song, X.,
Dai, Y.,
Qin, X.,
Deeply Supervised Depth Map Super-Resolution as Novel View Synthesis,
CirSysVideo(29), No. 8, August 2019, pp. 2323-2336.
IEEE DOI
1908
Color, Task analysis, Spatial resolution, Deconvolution, Cameras,
DH-HEMTs, Convolutional neural network, depth map,
novel view synthesis
BibRef
Liu, J.[Jing],
Sun, W.N.[Wan-Ning],
Su, Y.T.[Yu-Ting],
Jing, P.G.[Pei-Guang],
Yang, X.K.[Xiao-Kang],
BE-CALF: Bit-Depth Enhancement by Concatenating All Level Features of
DNN,
IP(28), No. 10, October 2019, pp. 4926-4940.
IEEE DOI
1909
convolutional neural nets, data visualisation, image enhancement,
image resolution, rendering (computer graphics),
skip connections
BibRef
Liu, J.[Jing],
Liu, P.P.[Ping-Ping],
Su, Y.T.[Yu-Ting],
Jing, P.G.[Pei-Guang],
Yang, X.K.[Xiao-Kang],
Spatiotemporal Symmetric Convolutional Neural Network for Video
Bit-Depth Enhancement,
MultMed(21), No. 9, September 2019, pp. 2397-2406.
IEEE DOI
1909
Image resolution, Convolutional codes, Spatiotemporal phenomena,
Dynamic range, Transforms, Distortion, Correlation, feature fusion
BibRef
Liu, J.[Jing],
Wen, X.[Xin],
Nie, W.Z.[Wei-Zhi],
Su, Y.T.[Yu-Ting],
Jing, P.G.[Pei-Guang],
Yang, X.K.[Xiao-Kang],
Residual-Guided Multiscale Fusion Network for Bit-Depth Enhancement,
CirSysVideo(32), No. 5, May 2022, pp. 2773-2786.
IEEE DOI
2205
Radio frequency, Feature extraction, Task analysis,
Image edge detection, Image restoration, Noise measurement,
feature fusion
BibRef
Nie, W.Z.[Wei-Zhi],
Wen, X.[Xin],
Liu, J.[Jing],
Su, Y.T.[Yu-Ting],
Iterative Residual Feature Refinement Network for Bit-Depth
Enhancement,
SPLetters(29), 2022, pp. 1387-1391.
IEEE DOI
2207
Feature extraction, Training, Image restoration, Task analysis,
Convolutional codes, Complexity theory, Data mining,
residual recovery
BibRef
Liu, J.[Jing],
Fan, Z.W.[Zhi-Wei],
Yang, Z.[Ziwen],
Su, Y.T.[Yu-Ting],
Yang, X.K.[Xiao-Kang],
Multi-Stage Spatio-Temporal Fusion Network for Fast and Accurate
Video Bit-Depth Enhancement,
MultMed(26), 2024, pp. 2444-2455.
IEEE DOI
2402
Feature extraction, Image reconstruction, Task analysis, Fuses,
Motion compensation, Distortion, Image color analysis, spatio-temporal fusion
BibRef
Liu, J.[Jing],
Yang, Z.[Ziwen],
Su, Y.T.[Yu-Ting],
Yang, X.K.[Xiao-Kang],
TANet: Target Attention Network for Video Bit-Depth Enhancement,
MultMed(24), 2022, pp. 4212-4223.
IEEE DOI
2209
Spatiotemporal phenomena, Task analysis, Feature extraction,
Image reconstruction, Distortion, Video bit-depth enhancement,
spatiotemporal feature fusion
BibRef
Huang, L.Q.[Li-Qin],
Zhang, J.J.[Jian-Jia],
Zuo, Y.F.[Yi-Fan],
Wu, Q.[Qiang],
Pyramid-Structured Depth MAP Super-Resolution Based on Deep
Dense-Residual Network,
SPLetters(26), No. 12, December 2019, pp. 1723-1727.
IEEE DOI
2001
convolutional neural nets, image reconstruction,
image representation, image resolution,
deep convolutional neural networks
BibRef
Zuo, Y.F.[Yi-Fan],
Wu, Q.[Qiang],
Fang, Y.M.[Yu-Ming],
An, P.[Ping],
Huang, L.Q.[Li-Qin],
Chen, Z.F.[Zhi-Feng],
Multi-Scale Frequency Reconstruction for Guided Depth Map
Super-Resolution via Deep Residual Network,
CirSysVideo(30), No. 2, February 2020, pp. 297-306.
IEEE DOI
2002
Color, Image reconstruction, Image edge detection,
Image resolution, Noise measurement, Dictionaries, Training,
batch-normalization
BibRef
Zuo, Y.F.[Yi-Fan],
Wang, H.[Hao],
Fang, Y.M.[Yu-Ming],
Huang, X.S.[Xiao-Shui],
Shang, X.[Xiwu],
Wu, Q.[Qiang],
MIG-Net: Multi-Scale Network Alternatively Guided by Intensity and
Gradient Features for Depth Map Super-Resolution,
MultMed(24), 2022, pp. 3506-3519.
IEEE DOI
2207
Superresolution, Image edge detection, Image color analysis,
Image coding, Color, Noise reduction, Dictionaries,
intensity-guided depth map super-resolution
BibRef
Li, B.C.[Bei-Chen],
Zhou, Y.[Yuan],
Zhang, Y.[Yeda],
Wang, A.[Aihua],
Depth image super-resolution based on joint sparse coding,
PRL(130), 2020, pp. 21-29.
Elsevier DOI
2002
Image super-resolution, Joint sparse coding
BibRef
Zhang, Y.[Yeda],
Zhou, Y.[Yuan],
Wang, A.[Aihua],
Wu, Q.[Qiong],
Hou, C.P.[Chun-Ping],
Joint nonlocal sparse representation for depth map super-resolution,
ICIP17(972-976)
IEEE DOI
1803
Color, Dictionaries, Estimation, Image reconstruction,
Principal component analysis, Spatial resolution,
sparse representation
BibRef
Wang, J.[Jin],
Xu, W.[Wei],
Cai, J.F.[Jian-Feng],
Zhu, Q.[Qing],
Shi, Y.H.[Yun-Hui],
Yin, B.C.[Bao-Cai],
Multi-Direction Dictionary Learning Based Depth Map Super-Resolution
With Autoregressive Modeling,
MultMed(22), No. 6, June 2020, pp. 1470-1484.
IEEE DOI
2005
Dictionaries, Adaptation models, Image edge detection, Color,
Machine learning, Cameras, Geometry, Depth map,
sparse representation
BibRef
Xu, W.[Wei],
Wang, J.[Jin],
Sun, L.H.[Long-Hua],
Zhu, Q.[Qing],
Depth Map Super-Resolution By Multi-Direction Dictionary and Joint
Regularization,
ICIP21(1839-1843)
IEEE DOI
2201
Visualization, Dictionaries, TV, Superresolution, Cameras, Depth map,
super-resolution (SR), dictionary training, regularization,
sparse representation
BibRef
Xu, W.[Wei],
Wang, J.[Jin],
Zhu, Q.[Qing],
Wu, X.[Xi],
Qi, Y.F.[Yi-Fei],
Depth map super-resolution via multiclass dictionary learning with
geometrical directions,
VCIP17(1-4)
IEEE DOI
1804
autoregressive processes, image colour analysis,
image reconstruction, image representation, image resolution,
super-resolution (SR)
BibRef
Gu, X.,
Guo, Y.,
Deligianni, F.,
Yang, G.,
Coupled Real-Synthetic Domain Adaptation for Real-World Deep Depth
Enhancement,
IP(29), 2020, pp. 6343-6356.
IEEE DOI
2006
Pipelines, Training, Deep learning, Sensors, Degradation,
Adaptation models, Depth enhancement, real-world, denoising,
deep learning
BibRef
Ye, X.C.[Xin-Chen],
Sun, B.[Baoli],
Wang, Z.H.[Zhi-Hui],
Yang, J.Y.[Jing-Yu],
Xu, R.[Rui],
Li, H.J.[Hao-Jie],
Li, B.[Baopu],
PMBANet: Progressive Multi-Branch Aggregation Network for Scene Depth
Super-Resolution,
IP(29), 2020, pp. 7427-7442.
IEEE DOI
2007
Depth map, super-resolution, aggregation, progressive, multi-branch
BibRef
Ye, X.C.[Xin-Chen],
Guo, Y.J.[Yan-Jun],
Sun, B.[Baoli],
Xu, R.[Rui],
Wang, Z.H.[Zhi-Hui],
Li, H.J.[Hao-Jie],
C2ANet: Cross-Scale and Cross-Modality Aggregation Network for Scene
Depth Super-Resolution,
MultMed(26), 2024, pp. 2574-2584.
IEEE DOI
2402
Color, Image color analysis, Task analysis, Feature extraction,
Image reconstruction, Spatial resolution, Aggregates, Alignment,
depth super-resolution
BibRef
Li, T.[Tao],
Lin, H.W.[Hong-Wei],
Dong, X.C.[Xiu-Cheng],
Zhang, X.H.[Xiao-Hua],
Depth image super-resolution using correlation-controlled color
guidance and multi-scale symmetric network,
PR(107), 2020, pp. 107513.
Elsevier DOI
2008
Depth image super-resolution,
Deep convolutional neural network, Encoder-decoder structure,
Channel correlation
BibRef
Deng, X.,
Song, P.,
Rodrigues, M.R.D.,
Dragotti, P.L.,
RADAR: Robust Algorithm for Depth Image Super Resolution Based on FRI
Theory and Multimodal Dictionary Learning,
CirSysVideo(30), No. 8, August 2020, pp. 2447-2462.
IEEE DOI
2008
Image resolution, Training, Machine learning, Color,
Image reconstruction, Noise measurement, Image edge detection,
multimodal image processing
BibRef
Haefner, B.[Bjoern],
Peng, S.Y.[Song-You],
Verma, A.[Alok],
Quéau, Y.[Yvain],
Cremers, D.[Daniel],
Photometric Depth Super-Resolution,
PAMI(42), No. 10, October 2020, pp. 2453-2464.
IEEE DOI
2009
Image resolution, Lighting, Shape, Training, Cameras, Color,
Frequency measurement, RGB-D cameras, depth super-resolution,
deep learning
BibRef
Sang, L.,
Haefner, B.,
Cremers, D.,
Inferring Super-Resolution Depth from a Moving Light-Source Enhanced
RGB-D Sensor: A Variational Approach,
WACV20(1-10)
IEEE DOI
2006
Cameras, Lighting, Image resolution, Light sources, Geometry,
Light emitting diodes, Calibration
BibRef
Jiang, Z.Y.[Zhong-Yu],
Yue, H.J.[Huan-Jing],
Lai, Y.K.[Yu-Kun],
Yang, J.Y.[Jing-Yu],
Hou, Y.H.[Yong-Hong],
Hou, C.P.[Chun-Ping],
Deep edge map guided depth super resolution,
SP:IC(90), 2021, pp. 116040.
Elsevier DOI
2012
Super resolution, Depth map, Edge prediction, Disentangling
BibRef
Zuo, Y.,
Fang, Y.,
Yang, Y.,
Shang, X.,
Wu, Q.,
Depth Map Enhancement by Revisiting Multi-Scale Intensity Guidance
Within Coarse-to-Fine Stages,
CirSysVideo(30), No. 12, December 2020, pp. 4676-4687.
IEEE DOI
2012
Color, Image edge detection, Feature extraction,
Image reconstruction, Encoding, Dictionaries,
dense connection
BibRef
Yeo, Y.J.,
Sagong, M.C.,
Shin, Y.G.,
Jung, S.W.,
Ko, S.J.,
Simple Yet Effective Way for Improving the Performance of Depth Map
Super-Resolution,
SPLetters(27), 2020, pp. 2099-2103.
IEEE DOI
2012
Image color analysis, Color, Convolution, Feature extraction, PSNR,
Image edge detection, Standards, Depth map super-resolution (SR),
deep learning
BibRef
Li, S.M.[Shi-Ming],
Ge, X.M.[Xu-Ming],
Hu, H.[Han],
Zhu, Q.[Qing],
Laplacian fusion approach of multi-source point clouds for detail
enhancement,
PandRS(171), 2021, pp. 385-396.
Elsevier DOI
2012
Multi-sources, Point Clouds, Reconstruction, Laplacian fusion
BibRef
Zuo, Y.,
Fang, Y.,
An, P.,
Shang, X.,
Yang, J.,
Frequency-Dependent Depth Map Enhancement via Iterative Depth-Guided
Affine Transformation and Intensity-Guided Refinement,
MultMed(23), 2021, pp. 772-783.
IEEE DOI
2102
Color, Image edge detection, Image resolution, Optimization,
Robustness, Encoding, Dictionaries, dense connection
BibRef
Zhang, F.[Fan],
Liu, N.[Na],
Chang, L.[Liang],
Duan, F.Q.[Fu-Qing],
Deng, X.M.[Xiao-Ming],
Edge-guided single facial depth map super-resolution using CNN,
IET-IPR(14), No. 17, 24 December 2020, pp. 4708-4716.
DOI Link
2104
BibRef
Li, Y.H.[Yin-Hao],
Iwamoto, Y.[Yutaro],
Lin, L.F.[Lan-Fen],
Xu, R.[Rui],
Tong, R.F.[Ruo-Feng],
Chen, Y.W.[Yen-Wei],
VolumeNet: A Lightweight Parallel Network for Super-Resolution of MR
and CT Volumetric Data,
IP(30), 2021, pp. 4840-4854.
IEEE DOI
2105
BibRef
Li, X.Q.[Xiao-Qiang],
Liu, J.[Jitao],
Dai, S.M.[Song-Min],
Point cloud super-resolution based on geometric constraints,
IET-CV(15), No. 4, 2021, pp. 312-321.
DOI Link
2106
BibRef
Zhang, Y.Y.[Ying-Ying],
Ren, C.[Chao],
Chen, H.G.[Hong-Gang],
Zhu, C.[Ce],
Liu, K.[Kai],
Single depth map super-resolution via joint non-local self-similarity
modeling and local multi-directional gradient-guided regularization,
SP:IC(97), 2021, pp. 116313.
Elsevier DOI
2107
Single depth map, Super-resolution, Non-local self-similarity,
Local constraint, Multi-directional gradient-guided regularization
BibRef
Zhang, R.C.[Rui-Chen],
Bian, S.F.[Shao-Feng],
Li, H.[Houpu],
RSPCN: Super-Resolution of Digital Elevation Model Based on Recursive
Sub-Pixel Convolutional Neural Networks,
IJGI(10), No. 8, 2021, pp. xx-yy.
DOI Link
2108
BibRef
Zhou, A.[Annan],
Chen, Y.[Yumin],
Wilson, J.P.[John P.],
Su, H.[Heng],
Xiong, Z.[Zhexin],
Cheng, Q.[Qishan],
An Enhanced Double-Filter Deep Residual Neural Network for Generating
Super Resolution DEMs,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link
2109
BibRef
Deng, Q.W.[Qin-Wen],
Zhang, S.Y.[Song-Yang],
Ding, Z.[Zhi],
Point Cloud Resampling via Hypergraph Signal Processing,
SPLetters(28), 2021, pp. 2117-2121.
IEEE DOI
2112
Kernel, Surface reconstruction,
Tensors, Feature extraction, Signal processing algorithms, virtual reality
BibRef
Zhong, Z.W.[Zhi-Wei],
Liu, X.M.[Xian-Ming],
Jiang, J.J.[Jun-Jun],
Zhao, D.B.[De-Bin],
Chen, Z.W.[Zhi-Wen],
Ji, X.Y.[Xiang-Yang],
High-Resolution Depth Maps Imaging via Attention-Based Hierarchical
Multi-Modal Fusion,
IP(31), 2022, pp. 648-663.
IEEE DOI
2201
Image reconstruction, Feature extraction, Convolution,
Superresolution, Kernel, Collaboration, Depth map super-resolution,
bi-directional feature propagation
BibRef
Yang, B.S.[Bi-Sheng],
Li, J.P.[Jian-Ping],
A hierarchical approach for refining point cloud quality of a low
cost UAV LiDAR system in the urban environment,
PandRS(183), 2022, pp. 403-421.
Elsevier DOI
2201
Low cost, Unmanned aerial vehicle (UAV),
Light detection and ranging (LiDAR), Point clouds, Matching
BibRef
Tao, Y.[Yu],
Xiong, S.T.[Si-Ting],
Muller, J.P.[Jan-Peter],
Michael, G.[Greg],
Conway, S.J.[Susan J.],
Paar, G.[Gerhard],
Cremonese, G.[Gabriele],
Thomas, N.[Nicolas],
Subpixel-Scale Topography Retrieval of Mars Using Single-Image DTM
Estimation and Super-Resolution Restoration,
RS(14), No. 2, 2022, pp. xx-yy.
DOI Link
2201
BibRef
Borges, T.M.[Tomás M.],
Garcia, D.C.[Diogo C.],
de Queiroz, R.L.[Ricardo L.],
Fractional Super-Resolution of Voxelized Point Clouds,
IP(31), 2022, pp. 1380-1390.
IEEE DOI
2202
Geometry, Point cloud compression,
Rendering (computer graphics), Octrees, Superresolution, resampling
BibRef
Liu, P.[Peng],
Zhang, Z.H.[Zong-Hua],
Meng, Z.Z.[Zhao-Zong],
Gao, N.[Nan],
Deformable Enhancement and Adaptive Fusion for Depth Map
Super-Resolution,
SPLetters(29), 2022, pp. 204-208.
IEEE DOI
2202
Superresolution, Convolution, Signal resolution, Image restoration,
Convolutional neural networks, fusion
BibRef
Yang, Y.X.[Yu-Xiang],
Cao, Q.[Qi],
Zhang, J.[Jing],
Tao, D.C.[Da-Cheng],
CODON: On Orchestrating Cross-Domain Attentions for Depth
Super-Resolution,
IJCV(130), No. 2, February 2022, pp. 267-284.
Springer DOI
2202
BibRef
Kalenjuk, S.[Slaven],
Lienhart, W.[Werner],
A Method for Efficient Quality Control and Enhancement of Mobile
Laser Scanning Data,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link
2202
BibRef
Lin, X.[Xu],
Zhang, Q.Q.[Qing-Qing],
Wang, H.Y.[Hong-Yue],
Yao, C.L.[Chao-Long],
Chen, C.X.[Chang-Xin],
Cheng, L.[Lin],
Li, Z.X.[Zhao-Xiong],
A DEM Super-Resolution Reconstruction Network Combining Internal and
External Learning,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link
2205
BibRef
Wang, J.[Jin],
Sun, L.H.[Long-Hua],
Xiong, R.Q.[Rui-Qin],
Shi, Y.H.[Yun-Hui],
Zhu, Q.[Qing],
Yin, B.C.[Bao-Cai],
Depth Map Super-Resolution Based on Dual Normal-Depth Regularization
and Graph Laplacian Prior,
CirSysVideo(32), No. 6, June 2022, pp. 3304-3318.
IEEE DOI
2206
Color, Laplace equations, Image edge detection, Optimization,
Image restoration, Image reconstruction, Cameras, Depth map,
reweighted graph Laplacian regularizer (RWGLR)
BibRef
Zhang, Y.F.[Yi-Fan],
Yu, W.H.[Wen-Hao],
Zhu, D.[Di],
Terrain feature-aware deep learning network for digital elevation
model superresolution,
PandRS(189), 2022, pp. 143-162.
Elsevier DOI
2206
DEM superresolution, Terrain features,
Explicit terrain optimization, Deformable convolution
BibRef
Murayama, M.[Masahiro],
Higashiyama, T.[Toyohiro],
Harazono, Y.[Yuki],
Ishii, H.[Hirotake],
Shimoda, H.[Hiroshi],
Okido, S.[Shinobu],
Taruta, Y.[Yasuyoshi],
Depth Image Noise Reduction and Super-Resolution by Pixel-Wise
Multi-Frame Fusion,
IEICE(E105-D), No. 6, June 2022, pp. 1211-1224.
WWW Link.
2206
BibRef
Dinesh, C.[Chinthaka],
Cheung, G.[Gene],
Bajic, I.V.[Ivan V.],
Point Cloud Video Super-Resolution via Partial Point Coupling and
Graph Smoothness,
IP(31), 2022, pp. 4117-4132.
IEEE DOI
2206
BibRef
Earlier:
3D Point Cloud Super-Resolution via Graph Total Variation on Surface
Normals,
ICIP19(4390-4394)
IEEE DOI
1910
graph signal processing, point cloud super-resolution,
graph total variation, convex optimization
Geometry, Noise reduction, Laplace equations, Image restoration,
Surface treatment, Computational modeling, 3D point cloud,
numerical linear algebra
BibRef
Yang, L.[Ling],
Zhang, F.[Fubo],
Zhang, Z.[Zhuo],
Chen, L.Y.[Long-Yong],
Wang, D.W.[Da-Wei],
Yang, Y.Q.[Ya-Qian],
Li, Z.H.[Zhen-Hua],
Elevation Resolution Enhancement Method Using Non-Ideal Linear Motion
Error of Airborne Array TomoSAR,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link
2206
BibRef
Song, X.B.[Xi-Bin],
Zhou, D.F.[Ding-Fu],
Li, W.[Wei],
Dai, Y.C.[Yu-Chao],
Liu, L.[Liu],
Li, H.D.[Hong-Dong],
Yang, R.G.[Rui-Gang],
Zhang, L.J.[Liang-Jun],
WAFP-Net: Weighted Attention Fusion Based Progressive Residual
Learning for Depth Map Super-Resolution,
MultMed(24), 2022, pp. 4113-4127.
IEEE DOI
2208
BibRef
Earlier: A1, A4, A2, A5, A3, A6, A7, Only:
Channel Attention Based Iterative Residual Learning for Depth Map
Super-Resolution,
CVPR20(5630-5639)
IEEE DOI
2008
Degradation, Superresolution, Color, Feature extraction, Laser radar,
Image edge detection, Attention fusion, depth, super-resolution,
residual learning.
Color, Kernel, Image resolution, Feature extraction
BibRef
Zhang, X.[Xue],
Cheung, G.[Gene],
Pang, J.H.[Jia-Hao],
Sanghvi, Y.[Yash],
Gnanasambandam, A.[Abhiram],
Chan, S.H.[Stanley H.],
Graph-Based Depth Denoising and Dequantization for Point Cloud
Enhancement,
IP(31), 2022, pp. 6863-6878.
IEEE DOI
2212
Sensors, Noise reduction, Point cloud compression,
Quantization (signal), Noise measurement, Image sensors,
graph signal processing
BibRef
Zhang, X.[Xue],
Cheung, G.[Gene],
Pang, J.H.[Jia-Hao],
Tian, D.,
3D Point Cloud Enhancement Using Graph-Modelled Multiview Depth
Measurements,
ICIP20(3314-3318)
IEEE DOI
2011
Cameras, Sensors, Optimization, Measurement, Noise reduction,
Laplace equations, 3D point cloud, convex optimization
BibRef
Agresti, G.[Gianluca],
Schäfer, H.[Henrik],
Sartor, P.[Piergiorgio],
Incesu, Y.[Yalcin],
Zanuttigh, P.[Pietro],
Unsupervised Domain Adaptation of Deep Networks for ToF Depth
Refinement,
PAMI(44), No. 12, December 2022, pp. 9195-9208.
IEEE DOI
2212
Noise reduction, Distortion, Frequency modulation,
Frequency-domain analysis, Deep learning, Thermal sensors,
adversarial learning
BibRef
Wang, H.T.[Hao-Tian],
Yang, M.[Meng],
Lan, X.G.[Xu-Guang],
Zhu, C.[Ce],
Zheng, N.N.[Nan-Ning],
Depth Map Recovery Based on a Unified Depth Boundary Distortion Model,
IP(31), 2022, pp. 7020-7035.
IEEE DOI
2212
Image analysis, Nonlinear distortion, Sensors, Task analysis,
Superresolution, Learning systems, Indexes, Depth map recovery,
depth super-resolution
BibRef
Xu, W.[Wei],
Zhu, Q.[Qing],
Qi, N.[Na],
Depth Map Super-Resolution via Joint Local Gradient and Nonlocal
Structural Regularizations,
CirSysVideo(32), No. 12, December 2022, pp. 8297-8311.
IEEE DOI
2212
Dictionaries, Cameras, Color, TV, Image edge detection, Task analysis,
Image color analysis, Depth map, dictionary learning, super-resolution
BibRef
Kim, J.[Jiwan],
Kim, M.C.[Min-Chang],
Shin, Y.G.[Yeong-Gil],
Chung, M.Y.[Min-Young],
Accurate depth image generation via overfit training of point cloud
registration using local frame sets,
CVIU(226), 2023, pp. 103588.
Elsevier DOI
2212
Depth image enhancement, Enhanced depth dataset, RGB-D image,
Unsupervised depth registration
BibRef
Wang, J.[Jun],
Liu, P.L.[Pei-Lin],
Wen, F.[Fei],
Self-Supervised Learning for RGB-Guided Depth Enhancement by
Exploiting the Dependency Between RGB and Depth,
IP(32), 2023, pp. 159-174.
IEEE DOI
2301
Degradation, Noise reduction, Image enhancement, Sensors, Filling,
Interference, Imaging, Depth image enhancement, RGB-guided,
mutual information
BibRef
Wang, K.[Ke],
Zhao, L.J.[Li-Jun],
Zhang, J.J.[Jin-Jing],
Zhang, J.L.[Jia-Long],
Wang, A.H.[An-Hong],
Bai, H.H.[Hui-Hui],
Joint depth map super-resolution method via deep hybrid-cross
guidance filter,
PR(136), 2023, pp. 109260.
Elsevier DOI
2301
Joint image filter, Depth image, Super-resolution,
Hybrid-cross guidance, Space-aware group-compensation
BibRef
Han, X.Y.[Xiao-Yi],
Ma, X.C.[Xiao-Chuan],
Li, H.[Houpu],
Chen, Z.L.[Zhan-Long],
A Global-Information-Constrained Deep Learning Network for Digital
Elevation Model Super-Resolution,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link
2301
BibRef
Sarmad, M.[Muhammad],
Ruspini, L.C.[Leonardo Carlos],
Lindseth, F.[Frank],
SIT-SR 3D: Self-supervised slice interpolation via transfer learning
for 3D volume super-resolution,
PRL(166), 2023, pp. 97-104.
Elsevier DOI
2302
Super-resolution, Digital rock analysis, Self-supervised learning
BibRef
Chen, G.D.[Guo-Dong],
Chen, Y.[Yumin],
Wilson, J.P.[John P.],
Zhou, A.[Annan],
Chen, Y.[Yuejun],
Su, H.[Heng],
An Enhanced Residual Feature Fusion Network Integrated with a Terrain
Weight Module for Digital Elevation Model Super-Resolution,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link
2303
BibRef
Lim, S.G.[Sung-Gyun],
Kim, D.H.[Dong-Ha],
Oh, K.J.[Kwan-Jung],
Lee, G.S.[Gwang-Soon],
Jeong, J.Y.[Jun Young],
Kim, J.G.[Jae-Gon],
Wider Depth Dynamic Range Using Occupancy Map Correction for Immersive
Video Coding,
IEICE(E106-D), No. 5, May 2023, pp. 1102-1105.
WWW Link.
2305
BibRef
Li, H.H.[Hong-Hao],
Zhou, X.[Xiran],
Yan, Z.G.[Zhi-Gang],
mapSR: A Deep Neural Network for Super-Resolution of Raster Map,
IJGI(12), No. 7, 2023, pp. xx-yy.
DOI Link
2308
BibRef
Zhong, Z.W.[Zhi-Wei],
Liu, X.M.[Xian-Ming],
Jiang, J.J.[Jun-Jun],
Zhao, D.B.[De-Bin],
Ji, X.Y.[Xiang-Yang],
Guided Depth Map Super-Resolution: A Survey,
Surveys(55), No. 14s, July 2023, pp. xx-yy.
DOI Link
2309
Survey, Depth Super-Resolution.
Survey, Super-Resolution. learning, prior, filtering, survey, Guided depth map super-resolution
BibRef
Xu, Z.Q.[Zi-Qiang],
Qian, Y.Y.[Yuan-Yuan],
Yang, T.P.[Tai-Ping],
Tang, F.Y.[Fu-Ying],
Luo, Y.H.[Yu-Han],
Si, F.Q.[Fu-Qi],
Unusual Enhancement of the Optical Depth on the Continental Shelf
Depth Latitudinal Variation in the Stratospheric Polar Vortex,
RS(15), No. 16, 2023, pp. 4054.
DOI Link
2309
BibRef
Wen, X.[Xin],
Nie, W.Z.[Wei-Zhi],
Liu, J.[Jing],
Su, Y.T.[Yu-Ting],
MRFT: Multiscale Recurrent Fusion Transformer Based Prior Knowledge
for Bit-Depth Enhancement,
CirSysVideo(33), No. 10, October 2023, pp. 5562-5575.
IEEE DOI
2310
BibRef
Qiao, X.[Xin],
Ge, C.Y.[Chen-Yang],
Zhang, Y.[Youmin],
Zhou, Y.H.[Yan-Hui],
Tosi, F.[Fabio],
Poggi, M.[Matteo],
Mattoccia, S.[Stefano],
Depth super-resolution from explicit and implicit high-frequency
features,
CVIU(237), 2023, pp. 103841.
Elsevier DOI Code:
WWW Link.
2311
Guided depth super-resolution, CNN, Transformer, Multi-scale,
High-frequency information
BibRef
Xu, D.[Dan],
Fan, X.P.[Xiao-Peng],
Zhao, D.B.[De-Bin],
Gao, W.[Wen],
Multiscale and multidirection depth map super resolution with
semantic inference,
IET-IPR(17), No. 13, 2023, pp. 3670-3687.
DOI Link
2311
image enhancement, image fusion, image processing,
image reconstruction, interpolation, stereo image processing
BibRef
Xing, J.[Jinrui],
Yuan, H.[Hui],
Hamzaoui, R.[Raouf],
Liu, H.[Hao],
Hou, J.H.[Jun-Hui],
GQE-Net: A Graph-Based Quality Enhancement Network for Point Cloud
Color Attribute,
IP(32), 2023, pp. 6303-6317.
IEEE DOI Code:
WWW Link.
2311
BibRef
Ma, J.[Ji],
Chen, J.J.[Jin-Jin],
Pix2PixSSR: Spatial super-resolution synthesis and visualization for
time-varying volumetric data,
IET-IPR(18), No. 1, 2024, pp. 59-76.
DOI Link
2401
data visualisation, signal synthesis, supervised learning, time series
BibRef
Ma, J.[Ji],
Ye, Y.H.[Yu-Hao],
Chen, J.J.[Jin-Jin],
Self-attention residual network-based spatial super-resolution
synthesis for time-varying volumetric data,
IET-IPR(18), No. 6, 2024, pp. 1579-1597.
DOI Link
2405
computer graphics, data visualisation, image processing
BibRef
Zhang, F.[Fan],
Liu, N.[Na],
Duan, F.Q.[Fu-Qing],
Coarse-to-Fine Depth Super-Resolution With Adaptive RGB-D Feature
Attention,
MultMed(26), 2024, pp. 2621-2633.
IEEE DOI
2402
BibRef
Earlier:
Coarse-to-fine Face Depth Super-Resolution with Attentive Feature
Selection,
ICPR22(3966-3972)
IEEE DOI
2212
Color, Image edge detection, Image color analysis, Feature extraction,
Superresolution, Pipelines, Image restoration, attention mechanism.
Visualization, Face recognition, Stacking,
Noise reduction, Robustness
BibRef
Wang, J.[Jin],
Li, C.Y.[Chen-Yang],
Shi, Y.H.[Yun-Hui],
Wang, D.[Dan],
Wu, M.E.[Mu-En],
Ling, N.[Nam],
Yin, B.C.[Bao-Cai],
MSF-Net: Multi-Scale Feedback Reconstruction for Guided Depth Map
Super-Resolution,
CirSysVideo(34), No. 2, February 2024, pp. 709-723.
IEEE DOI
2402
Feature extraction, Color, Superresolution, Image reconstruction,
Task analysis, Optimization, Data mining, Depth map, feedback
BibRef
Chen, C.[Chi],
Jin, A.[Ang],
Wang, Z.[Zhiye],
Zheng, Y.W.[Yong-Wei],
Yang, B.S.[Bi-Sheng],
Zhou, J.[Jian],
Xu, Y.H.[Yu-Hang],
Tu, Z.G.[Zhi-Gang],
SGSR-Net: Structure Semantics Guided LiDAR Super-Resolution Network
for Indoor LiDAR SLAM,
MultMed(26), 2024, pp. 1842-1854.
IEEE DOI
2402
Point cloud compression, Laser radar, Superresolution, Sensors,
Simultaneous localization and mapping, Task analysis, LiDAR
BibRef
Shi, Y.[Yuan],
Cao, H.Y.[Hui-Yun],
Xia, B.[Bin],
Zhu, R.[Rui],
Liao, Q.M.[Qing-Min],
Yang, W.M.[Wen-Ming],
DSR-Diff: Depth map super-resolution with diffusion model,
PRL(184), 2024, pp. 225-231.
Elsevier DOI Code:
WWW Link.
2408
Diffusion model, Guided depth super-resolution, Feature fusion,
Image reconstruction
BibRef
Zheng, X.[Xin],
Xu, Z.Q.[Zhao-Qi],
Yin, Q.[Qian],
Bao, Z.[Zelun],
Chen, Z.[Zhirui],
Wang, S.[Sizhu],
A Transformer-Unet Generative Adversarial Network for the
Super-Resolution Reconstruction of DEMs,
RS(16), No. 19, 2024, pp. 3676.
DOI Link
2410
BibRef
Görlitz, A.[Andreas],
Möller, M.[Michael],
Kolb, A.[Andreas],
Coherent Enhancement of Depth Images and Normal Maps Using
Second-Order Geometric Models on Weighted Finite Graphs,
3DV24(623-630)
IEEE DOI
2408
Geometry, Computational modeling, Image processing,
Noise reduction, Geometric modeling, Sensors
BibRef
Gkillas, A.[Alexandros],
Lalos, A.S.[Aris S.],
Ampeliotis, D.[Dimitris],
An Efficient Deep Unrolling Super-Resolution Network for Lidar
Automotive Scenes,
ICIP23(1840-1844)
IEEE DOI
2312
BibRef
Metzger, N.[Nando],
Daudt, R.C.[Rodrigo Caye],
Schindler, K.[Konrad],
Guided Depth Super-Resolution by Deep Anisotropic Diffusion,
CVPR23(18237-18246)
IEEE DOI
2309
BibRef
Sun, Z.H.[Zhang-Hao],
Ye, W.[Wei],
Xiong, J.H.[Jin-Hui],
Choe, G.[Gyeongmin],
Wang, J.L.[Jia-Liang],
Su, S.C.[Shuo-Chen],
Ranjan, R.[Rakesh],
Consistent Direct Time-of-Flight Video Depth Super-Resolution,
CVPR23(5075-5085)
IEEE DOI
2309
BibRef
Shtendel, G.[Gal],
Bhandari, A.[Ayush],
HDR-TOF: HDR Time-of-Flight Imaging via Modulo Acquisition,
ICIP22(3808-3812)
IEEE DOI
2211
Image sensors, Heuristic algorithms, Current measurement, Imaging,
Lighting, 3D/depth imaging, computational imaging,
time-of-flight
BibRef
Xie, W.[Wuyuan],
Huang, T.[Tengcong],
Wang, M.[Miaohui],
MNSRNet: Multimodal Transformer Network for 3D Surface
Super-Resolution,
CVPR22(12693-12702)
IEEE DOI
2210
Geometry, Surface reconstruction, Fuses, Superresolution,
Neural networks, Transforms, Vision applications and systems
BibRef
Zhao, Z.X.[Zi-Xiang],
Zhang, J.S.[Jiang-She],
Gu, X.[Xiang],
Tan, C.L.[Chang-Le],
Xu, S.[Shuang],
Zhang, Y.[Yulun],
Timofte, R.[Radu],
Van Gool, L.J.[Luc J.],
Spherical Space Feature Decomposition for Guided Depth Map
Super-Resolution,
ICCV23(12513-12524)
IEEE DOI Code:
WWW Link.
2401
BibRef
Zhao, Z.X.[Zi-Xiang],
Zhang, J.S.[Jiang-She],
Xu, S.[Shuang],
Lin, Z.[Zudi],
Pfister, H.[Hanspeter],
Discrete Cosine Transform Network for Guided Depth Map
Super-Resolution,
CVPR22(5687-5697)
IEEE DOI
2210
Convolutional codes, Image edge detection, Superresolution,
Feature extraction, Discrete cosine transforms, Data mining,
Computational photography
BibRef
Cui, M.L.[Mao-Lin],
Xie, W.Y.[Wu-Yuan],
Wang, M.H.[Miao-Hui],
Huang, T.C.[Teng-Cong],
Residual Geometric Feature Transform Network for 3D Surface
Super-Resolution,
3DV21(859-868)
IEEE DOI
2201
Point cloud compression, Surface reconstruction, Shape,
Soft sensors, Superresolution, Transforms
BibRef
Walecki, P.[Peter],
Taubin, G.[Gabriel],
GCSR: Gray Code Super-Resolution 3D Scanning,
3DV21(1156-1164)
IEEE DOI
2201
Solid modeling, Surface reconstruction, Superresolution,
Calibration, Reflective binary codes, Standards
BibRef
Chen, Z.Q.[Zhi-Qin],
Kim, V.G.[Vladimir G.],
Fisher, M.[Matthew],
Aigerman, N.[Noam],
Zhang, H.[Hao],
Chaudhuri, S.[Siddhartha],
DECOR-GAN: 3D Shape Detailization by Conditional Refinement,
CVPR21(15735-15744)
IEEE DOI
2111
Training, Geometry, Solid modeling,
Codes, Shape, Generative adversarial networks
BibRef
Sun, B.L.[Bao-Li],
Ye, X.C.[Xin-Chen],
Li, B.P.[Bao-Pu],
Li, H.J.[Hao-Jie],
Wang, Z.H.[Zhi-Hui],
Xu, R.[Rui],
Learning Scene Structure Guidance via Cross-Task Knowledge Transfer
for Single Depth Super-Resolution,
CVPR21(7788-7797)
IEEE DOI
2111
Training, Knowledge engineering, Runtime, Superresolution,
Network architecture, Task analysis
BibRef
Chen, Z.,
Liu, P.,
Wen, F.,
Wang, J.,
Ying, R.,
Restoration of Motion Blur in Time-of-Flight Depth Image Using Data
Alignment,
3DV20(820-828)
IEEE DOI
2102
Cameras, Phase measurement, Image restoration, Sensors,
Optical imaging, Adaptive optics, deblurring
BibRef
Kubade, A.[Ashish],
Patel, D.[Diptiben],
Sharma, A.[Avinash],
Rajan, K.S.,
AFN: Attentional Feedback Network Based 3d Terrain Super-resolution,
ACCV20(I:192-208).
Springer DOI
2103
BibRef
Hui, L.[Le],
Xu, R.[Rui],
Xie, J.[Jin],
Qian, J.J.[Jian-Jun],
Yang, J.[Jian],
Progressive Point Cloud Deconvolution Generation Network,
ECCV20(XV:397-413).
Springer DOI
2011
Code, Point Cloud.
WWW Link. Interpolation.
BibRef
Voynov, O.,
Artemov, A.,
Egiazarian, V.,
Notchenko, A.,
Bobrovskikh, G.,
Burnaev, E.,
Zorin, D.,
Perceptual Deep Depth Super-Resolution,
ICCV19(5652-5662)
IEEE DOI
2004
convolutional neural nets, image colour analysis,
image reconstruction, image resolution, image sampling, Optimization
BibRef
Li, J.,
Zhang, X.,
Tran, T.,
Point Cloud Denoising Based on Tensor Tucker Decomposition,
ICIP19(4375-4379)
IEEE DOI
1910
Point cloud denoising, Tucker decomposition, Hard thresholding, HOOI algorithm
BibRef
Yan, S.[Shi],
Wu, C.L.[Cheng-Lei],
Wang, L.Z.[Li-Zhen],
Xu, F.[Feng],
An, L.[Liang],
Guo, K.W.[Kai-Wen],
Liu, Y.B.[Ye-Bin],
DDRNet: Depth Map Denoising and Refinement for Consumer Depth Cameras
Using Cascaded CNNs,
ECCV18(X: 155-171).
Springer DOI
1810
BibRef
Jeon, J.[Junho],
Lee, S.Y.[Seung-Yong],
Reconstruction-Based Pairwise Depth Dataset for Depth Image Enhancement
Using CNN,
ECCV18(XVI: 438-454).
Springer DOI
1810
BibRef
Rapp, J.,
Dawson, R.M.A.,
Goyal, V.K.,
Improving LIDAR Depth Resolution with Dither,
ICIP18(1553-1557)
IEEE DOI
1809
Laser radar, Quantization (signal), Photonics, Laser modes,
Laser noise, Measurement by laser beam, Detectors,
generalized Gaussian
BibRef
Bolsee, Q.,
Munteanu, A.,
CNN-based Denoising of Time-Of-Flight Depth Images,
ICIP18(510-514)
IEEE DOI
1809
Noise reduction, Training, Convolution, Sensors, Cameras, Filtering,
Gaussian noise, Time-of-Flight, denoising, residual learning, Convolutional Neural Network
BibRef
Garcia, D.C.,
Fonseca, T.A.,
de Queiroz, R.L.,
Example-Based Super-Resolution for Point-Cloud Video,
ICIP18(2959-2963)
IEEE DOI
1809
Signal resolution,
Spatial resolution, Gain, Measurement, Octrees,
super-resolution (SR)
BibRef
Chen, R.,
Zhai, D.,
Liu, X.,
Zhao, D.,
Noise-Aware Super-Resolution of Depth Maps Via Graph-Based
Plug-And-Play Framework,
ICIP18(2536-2540)
IEEE DOI
1809
Image resolution, Laplace equations,
Task analysis, Image restoration, Image edge detection,
graph signal processing
BibRef
Xu, D.,
Fan, X.,
Zhao, D.,
Gao, W.,
Multiresolution Contourlet Transform Fusion Based Depth Map Super
Resolution,
ICIP18(2187-2191)
IEEE DOI
1809
Transforms, Spatial resolution, Color, Laplace equations, Fans,
contourlet transform, fusion,
super resolution
BibRef
Jin, Z.,
Luo, L.,
Tang, Y.,
Zou, W.,
Li, X.,
A CNN cascade for quality enhancement of compressed depth images,
VCIP17(1-4)
IEEE DOI
1804
convolution, data compression, feedforward neural nets,
filtering theory, image coding, image denoising, image recognition,
Quality enhancement
BibRef
Boubou, S.[Somar],
Narikiyo, T.[Tatsuo],
Kawanishi, M.[Michihiro],
Adaptive filter for denoising 3D data captured by depth sensors,
3DTV-CON17(1-4)
IEEE DOI
1804
adaptive filters, object recognition, signal denoising,
spatial variables measurement, support vector machines,
3D depth sensors
BibRef
Yang, J.,
Lan, H.,
Song, X.,
Li, K.,
Depth super-resolution via fully edge-augmented guidance,
VCIP17(1-4)
IEEE DOI
1804
edge detection, feedforward neural nets, image colour analysis,
image resolution, learning (artificial intelligence), CNNs,
fully guidance structure
BibRef
Zhang, H.T.,
Yu, J.,
Wang, Z.F.,
Depth map super-resolution using non-local higher-order
regularization with classified weights,
ICIP17(4043-4047)
IEEE DOI
1803
Adaptation models, Color, Feature extraction, Image color analysis,
Image edge detection, Image resolution, Tuning,
non-local generalized total variation
BibRef
Zhu, J.,
Zhang, J.,
Cao, Y.,
Wang, Z.,
Image guided depth enhancement via deep fusion and local linear
regularizaron,
ICIP17(4068-4072)
IEEE DOI
1803
Color, Correlation, Feature extraction, Image edge detection,
Image resolution, Noise reduction, Training, deep feature space,
local linear regularization
BibRef
Zhu, J.[Jiang],
Zhai, W.[Wei],
Cao, Y.[Yang],
Zha, Z.J.[Zheng-Jun],
Co-occurrent Structural Edge Detection for Color-Guided Depth Map
Super-Resolution,
MMMod18(I:93-105).
Springer DOI
1802
BibRef
Peng, S.,
Haefner, B.,
Quéau, Y.,
Cremers, D.,
Depth Super-Resolution Meets Uncalibrated Photometric Stereo,
CVPV17(2961-2968)
IEEE DOI
1802
Harmonic analysis, Image resolution, Lighting, Mathematical model,
Shape, Signal resolution, Standards
BibRef
Shiba, Y.,
Ono, S.,
Furukawa, R.,
Hiura, S.,
Kawasaki, H.,
Temporal Shape Super-Resolution by Intra-frame Motion Encoding Using
High-fps Structured Light,
ICCV17(115-123)
IEEE DOI
1802
calibration, cameras, image motion analysis, image reconstruction,
image resolution, image sensors, image sequences,
BibRef
Gu, S.,
Zuo, W.,
Guo, S.,
Chen, Y.,
Chen, C.,
Zhang, L.,
Learning Dynamic Guidance for Depth Image Enhancement,
CVPR17(712-721)
IEEE DOI
1711
Analytical models, Computational modeling, Image enhancement,
Image resolution, Sensors, Training, data
BibRef
Mieloch, D.,
Dziembowski, A.,
Grzelka, A.,
Stankiewicz, O.,
Domanski, M.,
Temporal enhancement of graph-based depth estimation method,
WSSIP17(1-4)
IEEE DOI
1707
Cameras, Estimation, Image processing, Motion segmentation,
Optimization, Transform coding,
Depth estimation, Image segmentation, Temporal, consistency
BibRef
Song, X.,
Huang, H.,
Zhong, F.,
Ma, X.,
Qin, X.,
Edge-guided depth map enhancement,
ICPR16(2758-2763)
IEEE DOI
1705
Color, Image color analysis, Image edge detection,
Noise measurement, Optimization, Sensors, Tensile, stress
BibRef
Song, X.B.[Xi-Bin],
Dai, Y.C.[Yu-Chao],
Qin, X.Y.[Xue-Ying],
Deep Depth Super-Resolution: Learning Depth Super-Resolution Using Deep
Convolutional Neural Network,
ACCV16(IV: 360-376).
Springer DOI
1704
BibRef
Ye, X.C.[Xin-Chen],
Song, X.L.[Xiao-Lin],
Yang, J.Y.[Jing-Yu],
Hou, C.P.[Chun-Ping],
Wang, Y.[Yao],
Depth recovery via decomposition of polynomial and piece-wise
constant signals,
VCIP16(1-4)
IEEE DOI
1701
Color
BibRef
Zhang, H.T.,
Kang, K.,
Wang, Z.F.,
Image guided depth map superresolution using non-local total
generalized variation,
VCIP16(1-4)
IEEE DOI
1701
Cameras
BibRef
Fu, M.,
Zhou, W.,
Depth map super-resolution via extended weighted mode filtering,
VCIP16(1-4)
IEEE DOI
1701
Histograms
BibRef
Akcay, O.,
Erenoglu, R.C.,
Erenoglu, O.,
Correction and Densification of UAS-Based Photogrammetric Thermal Point
Cloud,
ISPRS16(B3: 163-166).
DOI Link
1610
BibRef
Uruma, K.,
Konishi, K.,
Takahashi, T.,
Furukawa, T.,
High resolution depth image recovery algorithm based on the modeling
of the sum of an average distance image and a surface image,
ICIP16(2836-2840)
IEEE DOI
1610
Cameras
BibRef
Ferstl, D.[David],
Rother, M.,
Bischof, H.,
Variational Depth Superresolution Using Example-Based Edge
Representations,
ICCV15(513-521)
IEEE DOI
1602
Dictionaries
BibRef
Riegler, G.[Gernot],
Ferstl, D.[David],
Rüther, M.[Matthias],
Bischof, H.[Horst],
A Deep Primal-Dual Network for Guided Depth Super-Resolution,
BMVC16(xx-yy).
HTML Version.
1805
BibRef
Riegler, G.[Gernot],
Rüther, M.[Matthias],
Bischof, H.[Horst],
ATGV-Net: Accurate Depth Super-Resolution,
ECCV16(III: 268-284).
Springer DOI
1611
BibRef
Deng, X.W.[Xiao-Wei],
Wu, X.L.[Xiao-Lin],
Sparsity-based depth image restoration using surface priors and RGB-D
correlations,
ICIP15(3881-3885)
IEEE DOI
1512
Depth image, image restoration, sparsity, superresolution
BibRef
Lu, J.J.[Jia-Jun],
Forsyth, D.A.[David A.],
Sparse depth super resolution,
CVPR15(2245-2253)
IEEE DOI
1510
BibRef
Kwon, H.[Hyeok_Hyen],
Tai, Y.W.[Yu-Wing],
Lin, S.[Stephen],
Data-driven depth map refinement via multi-scale sparse
representation,
CVPR15(159-167)
IEEE DOI
1510
BibRef
Herrera, J.L.,
del-Blanco, C.R.,
Garcia, N.,
Edge-based depth gradient refinement for 2D to 3D learned prior
conversion,
3DTV-CON15(1-4)
IEEE DOI
1508
Clustering algorithms
BibRef
Schoenenberger, Y.,
Paratte, J.,
Vandergheynst, P.,
Graph-based denoising for time-varying point clouds,
3DTV-CON15(1-4)
IEEE DOI
1508
Manifolds
BibRef
Lee, G.G.C.,
Li, B.S.[Bo-Syun],
Chen, C.F.[Chun-Fu],
Content-adaptive depth map enhancement based on motion distribution,
VCIP14(482-485)
IEEE DOI
1504
filtering theory
BibRef
Joachimiak, M.[Michal],
Aflaki, P.[Payman],
Hannuksela, M.M.[Miska M.],
Gabbouj, M.[Moncef],
Evaluation of Depth-Based Super Resolution on Compressed Mixed
Resolution 3D Video,
BD3DCV14(227-237).
Springer DOI
1504
BibRef
Vianello, A.,
Michielin, F.,
Calvagno, G.,
Sartor, P.,
Erdler, O.,
Depth images super-resolution: An iterative approach,
ICIP14(3778-3782)
IEEE DOI
1502
Cameras;Color;Noise;Spatial resolution;Standards;Stereo vision
BibRef
dos Santos, L.T.A.[Leandro Tavares Aragão],
Loaiza Fernandez, M.E.[Manuel Eduardo],
Raposo, A.B.[Alberto Barbosa],
Generating Super-Resolved Depth Maps Using Low-Cost Sensors and RGB
Images,
ISVC14(II: 632-641).
Springer DOI
1501
BibRef
Li, L.[Li],
Zhang, C.M.[Cai-Ming],
A Nonlocal Filter-Based Hybrid Strategy for Depth Map Enhancement,
ICPR14(4394-4399)
IEEE DOI
1412
Color
BibRef
Wang, Y.C.[Yu-Cheng],
Di, H.J.[Hui-Jun],
Wang, B.J.[Bing-Jie],
Liang, W.[Wei],
Zhang, J.[Jian],
Jia, Y.D.[Yun-De],
Depth Super-resolution by Fusing Depth Imaging and Stereo Vision with
Structural Determinant Information Inference,
ICPR14(4212-4217)
IEEE DOI
1412
Art
BibRef
Ghesu, F.C.[Florin C.],
Köhler, T.[Thomas],
Haase, S.[Sven],
Hornegger, J.[Joachim],
Guided Image Super-Resolution: A New Technique for Photogeometric
Super-Resolution in Hybrid 3-D Range Imaging,
GCPR14(227-238).
Springer DOI
1411
BibRef
Hui, T.W.[Tak-Wai],
Ngan, K.N.[King Ngi],
Motion-Depth:
RGB-D Depth Map Enhancement with Motion and Depth in Complement,
CVPR14(3962-3969)
IEEE DOI
1409
BibRef
And:
Dense depth map generation using sparse depth data from normal flow,
ICIP14(3837-3841)
IEEE DOI
1502
BibRef
And:
Depth enhancement using RGB-D guided filtering,
ICIP14(3832-3836)
IEEE DOI
1502
Cameras.
Approximation methods
BibRef
Rana, P.K.,
Taghia, J.,
Flierl, M.,
Statistical methods for inter-view depth enhancement,
3DTV-CON14(1-4)
IEEE DOI
1409
image enhancement
BibRef
Correia, P.,
Marcelino, S.,
Assuncao, P.,
Faria, S.,
Soares, S.,
Pagliari, C.,
da Silva, E.,
Enhancement method for multiple description decoding of depth maps
subject to random loss,
3DTV-CON14(1-4)
IEEE DOI
1409
decoding
BibRef
Li, J.[Jing],
Lu, Z.C.[Zhi-Chao],
Zeng, G.[Gang],
Gan, R.[Rui],
Zha, H.B.[Hong-Bin],
Similarity-Aware Patchwork Assembly for Depth Image Super-resolution,
CVPR14(3374-3381)
IEEE DOI
1409
Assembly, Disassemble, Dpeth map super resolution, Self-similarity
BibRef
Joachimiak, M.,
Hannuksela, M.M.,
Gabbouj, M.,
View synthesis quality mapping for depth-based super resolution on
mixed resolution 3D video,
3DTV-CON14(1-4)
IEEE DOI
1409
image resolution
BibRef
Silva, J.W.[Jong Wan],
Gomes, L.[Leonardo],
Aguero, K.A.[Karl Apaza],
Bellon, O.R.P.[Olga R.P.],
Silva, L.[Luciano],
Real-time acquisition and super-resolution techniques on 3D
reconstruction,
ICIP13(2135-2139)
IEEE DOI
1402
3D reconstruction;real-time;super-resolution
BibRef
Zheng, H.,
Bouzerdoum, A.,
Phung, S.L.,
Depth image super-resolution using multi-dictionary sparse
representation,
ICIP13(957-961)
IEEE DOI
1402
Cameras
BibRef
Davoodianidaliki, M.,
Saadatseresht, M.,
Three Pre-Processing Steps to Increase the Quality of Kinect Range Data,
SMPR13(127-132).
DOI Link
1311
BibRef
Ismaeil, K.A.[Kassem Al],
Aouada, D.[Djamila],
Depth Super-Resolution by Enhanced Shift and Add,
CAIP13(II:100-107).
Springer DOI
1311
BibRef
Yu, L.F.[Lap-Fai],
Yeung, S.K.[Sai-Kit],
Tai, Y.W.[Yu-Wing],
Lin, S.[Stephen],
Shading-Based Shape Refinement of RGB-D Images,
CVPR13(1415-1422)
IEEE DOI
1309
BibRef
Hornacek, M.[Michael],
Rhemann, C.[Christoph],
Gelautz, M.[Margrit],
Rother, C.[Carsten],
Depth Super Resolution by Rigid Body Self-Similarity in 3D,
CVPR13(1123-1130)
IEEE DOI
1309
dense matching, depth super resolution, optimization
BibRef
Kim, J.,
Lee, J.K.,
Lee, K.M.,
Deeply-Recursive Convolutional Network for Image Super-Resolution,
CVPR16(1637-1645)
IEEE DOI
1612
BibRef
Hong, C.[Cheeun],
Kim, H.[Heewon],
Baik, S.[Sungyong],
Oh, J.[Junghun],
Lee, K.M.[Kyoung Mu],
DAQ: Channel-Wise Distribution-Aware Quantization for Deep Image
Super-Resolution Networks,
WACV22(913-922)
IEEE DOI
2202
Training, Deep learning, Quantization (signal),
Costs, Data acquisition, Superresolution,
Image Processing -> Image Restoration Deep Learning
-> Efficient Training and Inference Methods for Networks
BibRef
Lim, B.[Bee],
Son, S.[Sanghyun],
Kim, H.[Heewon],
Nah, S.[Seungjun],
Lee, K.M.[Kyoung Mu],
Enhanced Deep Residual Networks for Single Image Super-Resolution,
NTIRE17(1132-1140)
IEEE DOI
1709
Computational modeling, Computer architecture, Convolution,
Image reconstruction, Image resolution, Signal resolution, Training
BibRef
Kim, J.,
Lee, J.K.,
Lee, K.M.,
Accurate Image Super-Resolution Using Very Deep Convolutional
Networks,
CVPR16(1646-1654)
IEEE DOI
1612
BibRef
Lee, H.S.[Hee Seok],
Lee, K.M.[Kuoung Mu],
Dense 3D Reconstruction from Severely Blurred Images Using a Single
Moving Camera,
CVPR13(273-280)
IEEE DOI
1309
Dense 3D reconstruction, Image deblurring, Visual SLAM
BibRef
Lee, H.S.[Hee Seok],
Lee, K.M.[Kuoung Mu],
Simultaneous Super-Resolution of Depth and Images Using a Single
Camera,
CVPR13(281-288)
IEEE DOI
1309
Dense 3D reconstruction, Image super-resolution, Visual SLAM
See also Simultaneous Super-Resolution of Depth and Images Using a Single Camera.
BibRef
Nelson, K.,
Bhatti, A.,
Nahavandi, S.,
Super-resolution of a 3-dimensional scene from novel viewpoints,
ICARCV12(1380-1385).
IEEE DOI
1304
BibRef
Li, J.[Jing],
Lu, Z.C.[Zhi-Chao],
Zeng, G.[Gang],
Gan, R.[Rui],
Wang, L.[Long],
Zha, H.B.[Hong-Bin],
A Joint Learning-Based Method for Multi-view Depth Map Super
Resolution,
ACPR13(456-460)
IEEE DOI
1408
BibRef
Earlier: A1, A3, A4, A6, A5, Only:
A Bayesian Approach to Uncertainty-Based Depth Map Super Resolution,
ACCV12(IV:205-216).
Springer DOI
1304
image colour analysis
BibRef
Aodha, O.M.[Oisin Mac],
Campbell, N.D.F.[Neill D. F.],
Nair, A.[Arun],
Brostow, G.J.[Gabriel J.],
Patch Based Synthesis for Single Depth Image Super-Resolution,
ECCV12(III: 71-84).
Springer DOI
1210
BibRef
Gevrekci, M.[Murat],
Pakin, K.[Kubilay],
Depth map super resolution,
ICIP11(3449-3452).
IEEE DOI
1201
BibRef
Edeler, T.,
Ohliger, K.,
Hussmann, S.,
Mertins, A.,
Super resolution of time-of-flight depth images under consideration of
spatially varying noise variance,
ICIP09(1185-1188).
IEEE DOI
0911
BibRef
Awatsu, Y.[Yusaku],
Kawai, N.[Norihiko],
Sato, T.[Tomokazu],
Yokoya, N.[Naokazu],
Spatio-temporal Super-Resolution Using Depth Map,
SCIA09(696-705).
Springer DOI
0906
BibRef
Li, F.[Feng],
Yu, J.Y.[Jing-Yi],
Chai, J.X.[Jin-Xiang],
A hybrid camera for motion deblurring and depth map super-resolution,
CVPR08(1-8).
IEEE DOI
0806
BibRef
Yang, Q.X.[Qing-Xiong],
Yang, R.G.[Rui-Gang],
Davis, J.W.[James W.],
Nister, D.[David],
Spatial-Depth Super Resolution for Range Images,
CVPR07(1-8).
IEEE DOI
0706
BibRef
Zhang, S.[Song],
Royer, D.[Dale],
Yau, S.T.[Shing-Tung],
High-resolution, real-time-geometry video acquisition,
SigGraph06(Article 110).
WWW Link.
BibRef
0600
Zhang, S.[Song],
Huang, P.S.[Pei-Sen],
High-Resolution, Real-time 3D Shape Acquisition,
Sensor3D04(28).
IEEE DOI
0406
BibRef
Zhang, S.[Song],
High-Resolution, Real-Time 3-D Shape Measurement,
Ph.D.Thesis, 2005, State University of New York, Stony Brook.
BibRef
0500
Chapter on Motion Analysis -- Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion continues in
Point Cloud Up-Sampling .