11.14.3.8.9 Denoising, Range Images, Range, Depth Data

Chapter Contents (Back)
Depth Denoising. Noise.

Woiselle, A., Starck, J.L., Fadili, J.,
3-D Data Denoising and Inpainting with the Low-Redundancy Fast Curvelet Transform,
JMIV(39), No. 2, February 2011, pp. 121-139.
WWW Link. 1103
BibRef

Belhedi, A., Bartoli, A., Bourgeois, S., Gay-Bellile, V., Hamrouni, K., Sayd, P.,
Noise modelling in time-of-flight sensors with application to depth noise removal and uncertainty estimation in three-dimensional measurement,
IET-CV(9), No. 6, 2015, pp. 967-977.
DOI Link 1512
Gaussian distribution BibRef

Papari, G., Idowu, N., Varslot, T.,
Fast Bilateral Filtering for Denoising Large 3D Images,
IP(26), No. 1, January 2017, pp. 251-261.
IEEE DOI 1612
Gaussian distribution BibRef

Gao, Z., Li, Q., Zhai, R., Shan, M., Lin, F.,
Adaptive and Robust Sparse Coding for Laser Range Data Denoising and Inpainting,
CirSysVideo(26), No. 12, December 2016, pp. 2165-2175.
IEEE DOI 1612
Dictionaries BibRef

Garduņo-Ramon, M.A.[Marco Antonio], Terol-Villalobos, I.R.[Ivan R.], Osornio-Rios, R.A.[Roque A.], Morales-Hernandez, L.A.[Luis A.],
Methodology for filtering of depth maps based on the MCbR algorithm supported by color, shape and neighboring features,
SP:IC(70), 2019, pp. 220-232.
Elsevier DOI 1812
Time-of-flight, Inpainting, Closing by reconstruction, Mathematical morphology, Filtering, Noise classifier, Template matching BibRef

Zheng, Y.L.[Ying-Long], Li, G.Q.[Gui-Qing], Wu, S.H.[Shi-Hao], Liu, Y.X.[Yu-Xin], Gao, Y.F.[Yue-Fang],
Guided point cloud denoising via sharp feature skeletons,
VC(33), No. 6-8, June 2017, pp. 857-867.
WWW Link. 1706
BibRef

Mukherjee, P.S.[Partha Sarathi],
A multi-resolution and adaptive 3-D image denoising framework with applications in medical imaging,
SIViP(11), No. 7, October 2017, pp. 1379-1387.
WWW Link. 1708
BibRef

Cheng, Y.[Yang], Cao, J.[Jie], Hao, Q.[Qun], Xiao, Y.Q.[Yu-Qing], Zhang, F.H.[Fang-Hua], Xia, W.Z.[Wen-Ze], Zhang, K.[Kaiyu], Yu, H.Y.[Hao-Yong],
A Novel De-Noising Method for Improving the Performance of Full-Waveform LiDAR Using Differential Optical Path,
RS(9), No. 11, 2017, pp. xx-yy.
DOI Link 1712
BibRef

Li, H.X.[Hong-Xu], Chang, J.H.[Jian-Hua], Xu, F.[Fan], Liu, Z.X.[Zhen-Xing], Yang, Z.B.[Zhen-Bo], Zhang, L.[Luyao], Zhang, S.Y.[Shu-Yi], Mao, R.X.[Ren-Xiang], Dou, X.L.[Xiao-Lei], Liu, B.G.[Bing-Gang],
Efficient Lidar Signal Denoising Algorithm Using Variational Mode Decomposition Combined with a Whale Optimization Algorithm,
RS(11), No. 2, 2019, pp. xx-yy.
DOI Link 1902
BibRef

Gao, Z., Ji, H.,
Transform Learning Based Sparse Coding for LiDAR Data Denoising,
SPLetters(26), No. 3, March 2019, pp. 480-484.
IEEE DOI 1903
electrical engineering computing, iterative methods, learning (artificial intelligence), transform learning BibRef

Zeng, J., Cheung, G., Ng, M., Pang, J., Yang, C.,
3D Point Cloud Denoising Using Graph Laplacian Regularization of a Low Dimensional Manifold Model,
IP(29), 2020, pp. 3474-3489.
IEEE DOI 2002
Graph signal processing, point cloud denoising, low-dimensional manifold BibRef

Dinesh, C., Cheung, G., Bajic, I.V.,
Point Cloud Denoising via Feature Graph Laplacian Regularization,
IP(29), 2020, pp. 4143-4158.
IEEE DOI 2002
3D point cloud, graph signal processing, denoising, convex optimization, graph Laplacian regularizer BibRef

Casajus, P.H., Ritschel, T., Ropinski, T.,
Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning,
ICCV19(52-60)
IEEE DOI 2004
image denoising, unsupervised learning, unsupervised 3D point cloud denoising, unsupervised learning, Training BibRef

Eldesokey, A.[Abdelrahman], Felsberg, M.[Michael], Khan, F.S.[Fahad Shahbaz],
Confidence Propagation through CNNs for Guided Sparse Depth Regression,
PAMI(42), No. 10, October 2020, pp. 2423-2436.
IEEE DOI 2009
Convolution, Sensors, Task analysis, Computer architecture, Cameras, Autonomous vehicles, Reliability, Sparse data, CNNs, confidence propagation BibRef

Eldesokey, A., Felsberg, M., Holmquist, K., Persson, M.,
Uncertainty-Aware CNNs for Depth Completion: Uncertainty from Beginning to End,
CVPR20(12011-12020)
IEEE DOI 2008
Uncertainty, Task analysis, Probabilistic logic, Measurement uncertainty, Noise measurement, Convolution, Computer vision BibRef

Ibrahim, M.M.[Mostafa M.], Liu, Q.[Qiong], Yang, Y.[You],
Adaptive colour-guided non-local means algorithm for compound noise reduction of depth maps,
IET-IPR(14), No. 12, October 2020, pp. 2768-2779.
DOI Link 2010
BibRef


Pistilli, F.[Francesca], Fracastoro, G.[Giulia], Valsesia, D.[Diego], Magli, E.[Enrico],
Learning Graph-convolutional Representations for Point Cloud Denoising,
ECCV20(XX:103-118).
Springer DOI 2011
BibRef

Zhou, H., Chen, K., Zhang, W., Fang, H., Zhou, W., Yu, N.,
DUP-Net: Denoiser and Upsampler Network for 3D Adversarial Point Clouds Defense,
ICCV19(1961-1970)
IEEE DOI 2004
computer graphics, cryptography, feature extraction, Gaussian processes, image classification, image denoising, Measurement BibRef

Sterzentsenko, V., Saroglou, L., Chatzitofis, A., Thermos, S., Zioulis, N., Doumanoglou, A., Zarpalas, D., Daras, P.,
Self-Supervised Deep Depth Denoising,
ICCV19(1242-1251)
IEEE DOI 2004
Code, Depth Denoising.
WWW Link. cameras, computer vision, convolutional neural nets, data acquisition, image colour analysis, image denoising, Color BibRef

Hui, Z., Cheng, P., Wang, L., Xia, Y., Hu, H., Li, X.,
A Novel Denoising Algorithm for Airborne Lidar Point Cloud Based On Empirical Mode Decomposition,
Laser19(1021-1025).
DOI Link 1912
BibRef

Mugner, E., Seube, N.,
Denoising of 3d Point Clouds,
LC3D19(217-224).
DOI Link 1912
BibRef

Sarkar, K., Bernard, F.[Florian], Varanasi, K., Theobalt, C.[Christian], Stricker, D.,
Structured Low-Rank Matrix Factorization for Point-Cloud Denoising,
3DV18(444-453)
IEEE DOI 1812
approximation theory, image denoising, image reconstruction, image representation, iterative methods, 3D patches BibRef

Charron, N., Phillips, S., Waslander, S.L.,
De-noising of Lidar Point Clouds Corrupted by Snowfall,
CRV18(254-261)
IEEE DOI 1812
Laser radar, Snow, Noise reduction, Smoothing methods, Image segmentation, snow noise removal BibRef

Kim, Y.S.,
Closed-Form Solution of Simultaneous Denoising and Hole Filling of Depth Image,
ICIP18(968-972)
IEEE DOI 1809
Filtering, Image reconstruction, Image color analysis, Kernel, Filling, Cameras, Color, Depth recovery, denoising, hole filling, time-of-flight depth BibRef

Sarkar, K.[Kripasindhu], Hampiholi, B.[Basavaraj], Varanasi, K.[Kiran], Stricker, D.[Didier],
Learning 3D Shapes as Multi-layered Height-Maps Using 2D Convolutional Networks,
ECCV18(XVI: 74-89).
Springer DOI 1810
BibRef
Earlier: A1, A3, A4, Only:
3D Shape Processing by Convolutional Denoising Autoencoders on Local Patches,
WACV18(1925-1934)
IEEE DOI 1806
computational geometry, convolution, feedforward neural nets, image coding, image denoising, image reconstruction, BibRef

Liao, X., Zhang, X.,
Multi-scale mutual feature convolutional neural network for depth image denoise and enhancement,
VCIP17(1-4)
IEEE DOI 1804
cameras, feature extraction, image colour analysis, image denoising, image enhancement, mutual feature BibRef

Jaiswal, M.S.[Mayoore S.], Wang, Y.Y.[Yu-Ying], Sun, M.T.[Ming-Ting],
Object Boundary Based Denoising for Depth Images,
ICIAR17(125-133).
Springer DOI 1706
BibRef

Wolff, K., Kim, C., Zimmer, H., Schroers, C., Botsch, M., Sorkine-Hornung, O., Sorkine-Hornung, A.,
Point Cloud Noise and Outlier Removal for Image-Based 3D Reconstruction,
3DV16(118-127)
IEEE DOI 1701
Image reconstruction BibRef

Chapter on 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing continues in
Outpainting, Extrapolation .


Last update:Nov 23, 2020 at 10:27:11