18.4.3.7 Stereo Image Super Resolution

Chapter Contents (Back)
Stereo Super Resolution. Stereo Image Super Resolution. Super Resolution.
See also Range Data Super Resolution, Depth Super Resolution.

Chen, L.[Li], Tian, J.[Jing],
Depth image enlargement using an evolutionary approach,
SP:IC(28), No. 7, 2013, pp. 745-752.
Elsevier DOI 1307
BibRef
Earlier: A2, A1:
Depth image up-sampling using ant colony optimization,
ICPR12(3795-3798).
WWW Link. 1302
BibRef
And: A2, A1:
Bayesian image enlargement for mixed-resolution video,
ICPR12(3082-3085).
WWW Link. 1302
BibRef
Earlier: A2, A1:
Bayesian stereoscopic image resolution enhancement,
ICIP11(1505-1508).
IEEE DOI 1201
BibRef
Earlier: A2, A1:
Multi-focus image fusion using wavelet-domain statistics,
ICIP10(1205-1208).
IEEE DOI 1009
Depth map
See also Fusing remote sensing images using a trous wavelet transform and empirical mode decomposition. BibRef

Richter, T.[Thomas], Seiler, J.[Jurgen], Schnurrer, W.[Wolfgang], Kaup, A.[Andre],
Robust Super-Resolution for Mixed-Resolution Multiview Image Plus Depth Data,
CirSysVideo(26), No. 5, May 2016, pp. 814-828.
IEEE DOI 1605
BibRef
Earlier:
Super-resolution for mixed-resolution multiview image plus depth data using a novel two-stage high-frequency extrapolation method for occluded areas,
ICIP15(1880-1884)
IEEE DOI 1512
Mixed-Resolution, Signal Extrapolation, Stereo Imaging, Super-Resolution BibRef

Seiler, J., Jonscher, M., Ussmueller, T., Kaup, A.,
Increasing Imaging Resolution by Non-Regular Sampling and Joint Sparse Deconvolution and Extrapolation,
CirSysVideo(29), No. 2, February 2019, pp. 308-322.
IEEE DOI 1902
Image sensors, Sensors, Image resolution, Image reconstruction, Layout, Reconstruction algorithms, Prototypes, spare reconstruction BibRef

Richter, T.[Thomas], Kaup, A.[Andre],
Multiview super-resolution using high-frequency synthesis in case of low-framerate depth information,
VCIP12(1-6).
IEEE DOI 1302
BibRef

Richter, T., Habermann, A., Kaup, A.,
Super-resolution for mixed-resolution multiview images using a relative frequency response estimation method,
VCIP15(1-4)
IEEE DOI 1605
Cameras BibRef

Genser, N., Seiler, J.[Jurgen], Jonscher, M.[Markus], Kaup, A.,
Demonstration of rapid frequency selective reconstruction for image resolution enhancement,
ICIP17(4595-4595)
IEEE DOI 1803
Computers, Image reconstruction, Image resolution, Image sensors, Sensors BibRef

Schnurrer, W.[Wolfgang], Jonscher, M.[Markus], Seiler, J.[Jurgen], Richter, T.[Thomas], Batz, M., Kaup, A.[Andre],
Centroid adapted frequency selective extrapolation for reconstruction of lost image areas,
VCIP15(1-4)
IEEE DOI 1605
Adaptation models
See also Reducing randomness of non-regular sampling masks for image reconstruction. BibRef

Lei, J.J.[Jian-Jun], Zhang, Z.[Zhe], Fan, X.T.[Xiao-Ting], Yang, B.[Bolan], Li, X.X.[Xin-Xin], Chen, Y.[Ying], Huang, Q.M.[Qing-Ming],
Deep Stereoscopic Image Super-Resolution via Interaction Module,
CirSysVideo(31), No. 8, August 2021, pp. 3051-3061.
IEEE DOI 2108
Stereo image processing, Image reconstruction, Feature extraction, Correlation, Spatial resolution, deep learning BibRef

Ning, L.[Luyao], Wang, A.H.[An-Hong], Zhao, L.J.[Li-Jun], Xue, W.M.[Wei-Min], Bu, D.H.[Dong-Han],
MRANet: Multi-atrous residual attention Network for stereo image super-resolution,
JVCIR(77), 2021, pp. 103115.
Elsevier DOI 2106
Stereo cameras, Stereo image super-resolution, Discriminative ability, Parallax extraction, Attention mechanism BibRef

Dan, J.W.[Jia-Wang], Qu, Z.W.[Zhao-Wei], Wang, X.R.[Xiao-Ru], Gu, J.A.[Ji-Ahang],
A Disparity Feature Alignment Module for Stereo Image Super-Resolution,
SPLetters(28), 2021, pp. 1285-1289.
IEEE DOI 2107
Convolution, Superresolution, Training, Spatial resolution, Memory management, Graphics processing units, Training data, attention mechanism BibRef

Duan, C.Y.[Chen-Yang], Xiao, N.[Nanfeng],
Parallax-based second-order mixed attention for stereo image super-resolution,
IET-CV(16), No. 1, 2022, pp. 26-37.
DOI Link 2202
BibRef

Chen, C.Q.[Can-Qiang], Qing, C.M.[Chun-Mei], Xu, X.M.[Xiang-Min], Dickinson, P.[Patrick],
Cross Parallax Attention Network for Stereo Image Super-Resolution,
MultMed(24), 2022, pp. 202-216.
IEEE DOI 2202
Task analysis, Superresolution, Cameras, Visualization, Spatial resolution, Estimation, convolutional neural network BibRef

Zhu, X.Y.[Xiang-Yuan], Guo, K.[Kehua], Fang, H.[Hui], Chen, L.[Liang], Ren, S.[Sheng], Hu, B.[Bin],
Cross View Capture for Stereo Image Super-Resolution,
MultMed(24), 2022, pp. 3074-3086.
IEEE DOI 2206
Superresolution, Feature extraction, Image reconstruction, Spatial resolution, Task analysis, Visual perception, Training, spatial perception BibRef


Ma, L.[Li], Li, S.[Sumei],
Enhanced Back Projection Network Based Stereo Image Super-Resolution Considering Parallax Attention,
ICIP21(1834-1838)
IEEE DOI 2201
Superresolution, Estimation, Benchmark testing, Feature extraction, Task analysis, Image reconstruction, back projection network BibRef

Tosi, F.[Fabio], Liao, Y.[Yiyi], Schmitt, C.[Carolin], Geiger, A.[Andreas],
SMD-Nets: Stereo Mixture Density Networks,
CVPR21(8938-8948)
IEEE DOI 2111
Solid modeling, Uncertainty, Superresolution, Estimation, Pattern recognition, Spatial resolution BibRef

Gee, T.[Trevor], Gimel’farb, G.[Georgy], Woodward, A.[Alexander], Ababou, R.[Rachel], Strozzi, A.G.[Alfonso Gastelum], Delmas, P.[Patrice],
Guided Stereo to Improve Depth Resolution of a Small Baseline Stereo Camera Using an Image Sequence,
ACIVS20(480-491).
Springer DOI 2003
BibRef

Li, W.[Weifu], John, V.[Vijay], Mita, S.[Seiichi],
Enhancing Depth Quality of Stereo Vision using Deep Learning-based Prior Information of the Driving Environment,
ICPR21(7281-7286)
IEEE DOI 2105
Deep learning, Image segmentation, Roads, Graphics processing units, Real-time systems, Stereo vision, Pattern recognition BibRef

Jeon, D.S., Baek, S., Choi, I., Kim, M.H.,
Enhancing the Spatial Resolution of Stereo Images Using a Parallax Prior,
CVPR18(1721-1730)
IEEE DOI 1812
Spatial resolution, Image reconstruction, Imaging, Color, Image color analysis, Training BibRef

Li, J., You, S., Robles-Kelly, A.,
Stereo Super-Resolution via a Deep Convolutional Network,
DICTA17(1-7)
IEEE DOI 1804
convolution, image resolution, learning (artificial intelligence), neural nets, Training BibRef

Kimura, K.[Kazuto], Nagai, T.[Takayuki], Nagayoshi, H.[Hiroto], Sako, H.[Hiroshi],
Simultaneous Estimation of Super-Resolved Image and 3D Information using Multiple Stereo-Pair Images,
ICIP07(V: 417-420).
IEEE DOI 0709
BibRef

Or, S.H.[Siu Hang], Yu, Y.K.[Ying Kin], Wong, K.H.[Kin Hong], Chang, M.M.Y.,
Resolution Improvement from Stereo Images with 3D Pose Differences,
ICIP06(1733-1736).
IEEE DOI 0610
BibRef
And: A2, A3, A4, A1:
Computing Pose Sequences Directly from Videos,
ICIP06(2773-2776).
IEEE DOI 0610
BibRef

Mitra, K.[Kaushik], Veeraraghavan, A.[Ashok],
Light field denoising, light field superresolution and stereo camera based refocussing using a GMM light field patch prior,
CCD12(22-28).
IEEE DOI 1207
BibRef

Chapter on Motion Analysis -- Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion continues in
Range Data Super Resolution, Depth Super Resolution .


Last update:Aug 14, 2022 at 21:20:19