18.4.3.1 Neural Netowrks for Super Resolution

Chapter Contents (Back)
Super Resolution. Neural Networks.

Lu, Y.[Yao], Inamura, M.[Minoru], del Carmen Valdes, M.[Maria],
Super-resolution of the undersampled and subpixel shifted image sequence by a neural network,
IJIST(14), No. 1, 2004, pp. 8-15.
DOI Link 0406
BibRef

Miravet, C.[Carlos], Rodriguez, F.B.[Francisco B.],
A two-step neural-network based algorithm for fast image super-resolution,
IVC(25), No. 9, 1 September 2007, pp. 1449-1473.
Elsevier DOI 0707
Super-resolution; Multi-layer perceptron; Probabilistic neural network; Sequence processing; Image restoration BibRef

Miravet, C.[Carlos], Rodriguez, F.B.[Francisco B.],
A PCA-based super-resolution algorithm for short image sequences,
ICIP10(2025-2028).
IEEE DOI 1009
BibRef

Agrawal, D., Singhai, J.,
Multifocus image fusion using modified pulse coupled neural network for improved image quality,
IET-IPR(4), No. 6, December 2010, pp. 443-451.
DOI Link 1101
BibRef

Dong, C.[Chao], Loy, C.C.[Chen Change], He, K.[Kaiming], Tang, X.[Xiaoou],
Image Super-Resolution Using Deep Convolutional Networks,
PAMI(38), No. 2, February 2016, pp. 295-307.
IEEE DOI 1601
BibRef
Earlier:
Learning a Deep Convolutional Network for Image Super-Resolution,
ECCV14(IV: 184-199).
Springer DOI 1408
Convolutional codes See also Compression Artifacts Reduction by a Deep Convolutional Network. BibRef

Dong, C.[Chao], Loy, C.C.[Chen Change], Tang, X.[Xiaoou],
Accelerating the Super-Resolution Convolutional Neural Network,
ECCV16(II: 391-407).
Springer DOI 1611
BibRef

Hui, T.W.[Tak-Wai], Loy, C.C.[Chen Change], Tang, X.[Xiaoou],
Depth Map Super-Resolution by Deep Multi-Scale Guidance,
ECCV16(III: 353-369).
Springer DOI 1611
BibRef

Wang, L.F.[Ling-Feng], Huang, Z.[Zehao], Gong, Y.C.[Yong-Chao], Pan, C.H.[Chun-Hong],
Ensemble based deep networks for image super-resolution,
PR(68), No. 1, 2017, pp. 191-198.
Elsevier DOI 1704
Super-resolution BibRef

Huang, Z.[Zehao], Wang, L.F.[Ling-Feng], Meng, G., Pan, C.H.[Chun-Hong],
Image Super-Resolution Via Deep Dilated Convolutional Networks,
ICIP17(953-957)
IEEE DOI 1803
Acceleration, Convolution, Image reconstruction, Image resolution, Machine learning, Task analysis, Training, Acceleration, Super-Resolution BibRef

Yoon, Y., Jeon, H.G., Yoo, D., Lee, J.Y.[Joon-Young], Kweon, I.S.[In So],
Light-Field Image Super-Resolution Using Convolutional Neural Network,
SPLetters(24), No. 6, June 2017, pp. 848-852.
IEEE DOI 1705
BibRef
Earlier:
Learning a Deep Convolutional Network for Light-Field Image Super-Resolution,
CVPV15(57-65)
IEEE DOI 1602
Cameras, Convolution, Neural networks, Signal resolution, Spatial resolution, Training, Convolutional neural network, light-field (LF) image super-resolution (SR). Cameras; Image restoration; Lenses; Neural networks; Spatial resolution BibRef

Shi, Y.[Yukai], Wang, K.[Keze], Chen, C.Y.[Chong-Yu], Xu, L.[Li], Lin, L.[Liang],
Structure-Preserving Image Super-Resolution via Contextualized Multitask Learning,
MultMed(19), No. 12, December 2017, pp. 2804-2815.
IEEE DOI 1712
Context, Feature extraction, Image resolution, Image restoration, Interpolation, Neural networks, Training, Convolutional network, structure-preserving image super-resolution (SR) BibRef

Mei, S.H.[Shao-Hui], Yuan, X.[Xin], Ji, J.Y.[Jing-Yu], Zhang, Y.F.[Yi-Fan], Wan, S.[Shuai], Du, Q.[Qian],
Hyperspectral Image Spatial Super-Resolution via 3D Full Convolutional Neural Network,
RS(9), No. 11, 2017, pp. xx-yy.
DOI Link 1712
BibRef

Mei, S.H.[Shao-Hui], Yuan, X.[Xin], Ji, J.Y.[Jing-Yu], Wan, S.[Shuai], Hou, J., Du, Q.[Qian],
Hyperspectral image super-resolution via convolutional neural network,
ICIP17(4297-4301)
IEEE DOI 1803
Distortion, Hyperspectral imaging, Image reconstruction, Image restoration, Spatial resolution, Hyperspectral, super-resolution BibRef

Song, Q., Xiong, R., Liu, D., Xiong, Z.W.[Zhi-Wei], Wu, F.[Feng], Gao, W.,
Fast Image Super-Resolution via Local Adaptive Gradient Field Sharpening Transform,
IP(27), No. 4, April 2018, pp. 1966-1980.
IEEE DOI 1802
gradient methods, image resolution, image restoration, HR image, LR image, blurry gradient field, gradient profile structure, upsampling BibRef

Zhang, H.C.[Hao-Chen], Liu, D.[Dong], Xiong, Z.W.[Zhi-Wei],
Convolutional Neural Network-Based Video Super-Resolution for Action Recognition,
FG18(746-750)
IEEE DOI 1806
Image recognition, Optical imaging, Optical losses, Spatial resolution, Streaming media, Training, Action recognition, video super-resolution BibRef

Yu, H.C.[Hai-Chao], Liu, D.[Ding], Shi, H.H.[Hong-Hui], Yu, H.C.[Han-Chao], Wang, Z.Y.[Zhang-Yang], Wang, X.C.[Xin-Chao], Cross, B.[Brent], Bramler, M.[Matthew], Huang, T.S.[Thomas S.],
Computed Tomography Super-Resolution Using Convolutional Neural Networks,
ICIP17(3944-3948)
IEEE DOI 1803
computerised tomography, feature extraction, image reconstruction, image resolution, Super-resolution (SR) BibRef

Li, Y.[Yue], Liu, D.[Dong], Li, H.Q.[Hou-Qiang], Li, L.[Li], Li, Z.[Zhu], Wu, F.[Feng],
Learning a Convolutional Neural Network for Image Compact-Resolution,
IP(28), No. 3, March 2019, pp. 1092-1107.
IEEE DOI 1812
data compression, image coding, image reconstruction, image resolution, image sampling, neural nets, video coding, up-sampling BibRef

Fan, H.Z.[Han-Zhi], Liu, D.[Dong], Xiong, Z.W.[Zhi-Wei], Wu, F.[Feng],
Two-Stage Convolutional Neural Network for Light Field Super-Resolution,
ICIP17(1167-1171)
IEEE DOI 1803
Cameras, Convolutional neural networks, Correlation, Spatial resolution, Training, super-resolution BibRef

Cheong, J.Y., Park, I.K.,
Deep CNN-Based Super-Resolution Using External and Internal Examples,
SPLetters(24), No. 8, August 2017, pp. 1252-1256.
IEEE DOI 1708
convolution, image resolution, neural nets, deep CNN-based SISR method, deep CNN-based superresolution, deep convolutional neural network, global residual network, internal example-based SISR methods, Deep convolutional neural network (CNN), external example, internal example, single, image, super-resolution, (SISR) BibRef

Huang, Y., Shao, L., Frangi, A.F.,
Cross-Modality Image Synthesis via Weakly Coupled and Geometry Co-Regularized Joint Dictionary Learning,
MedImg(37), No. 3, March 2018, pp. 815-827.
IEEE DOI 1804
BibRef
Earlier:
Simultaneous Super-Resolution and Cross-Modality Synthesis of 3D Medical Images Using Weakly-Supervised Joint Convolutional Sparse Coding,
CVPR17(5787-5796)
IEEE DOI 1711
diseases, geometry, image matching, image registration, image representation, learning (artificial intelligence), sparse representation. Biomedical imaging, Convolutional codes, Image coding, Image reconstruction, Image resolution, Training BibRef

Zhang, F.[Fu], Cai, N.[Nian], Cen, G.D.[Guan-Dong], Li, F.Y.[Fei-Yang], Wang, H.[Han], Chen, X.[Xindu],
Image super-resolution via a novel cascaded convolutional neural network framework,
SP:IC(63), 2018, pp. 9-18.
Elsevier DOI 1804
Image super-resolution, Cascaded convolution neural network, Multi-scale feature mapping, Residual learning, Gradient clipping BibRef

Pouliot, D.[Darren], Latifovic, R.[Rasim], Pasher, J.[Jon], Duffe, J.[Jason],
Landsat Super-Resolution Enhancement Using Convolution Neural Networks and Sentinel-2 for Training,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Wang, Y., Liu, F., Zhang, K., Hou, G., Sun, Z., Tan, T.,
LFNet: A Novel Bidirectional Recurrent Convolutional Neural Network for Light-Field Image Super-Resolution,
IP(27), No. 9, September 2018, pp. 4274-4286.
IEEE DOI 1807
convolution, feedforward neural nets, image fusion, image reconstruction, image resolution, recurrent neural nets, super-resolution BibRef

Ren, C., He, X., Pu, Y.,
Nonlocal Similarity Modeling and Deep CNN Gradient Prior for Super Resolution,
SPLetters(25), No. 7, July 2018, pp. 916-920.
IEEE DOI 1807
convolution, feedforward neural nets, gradient methods, image resolution, AHNLTV, GA-GP approach, GSR, super resolution BibRef

Chu, J., Zhang, J., Lu, W., Huang, X.,
A Novel Multiconnected Convolutional Network for Super-Resolution,
SPLetters(25), No. 7, July 2018, pp. 946-950.
IEEE DOI 1807
convolution, feedforward neural nets, image representation, image resolution, optimisation, SISR tasks, super-resolution BibRef

Zhao, J.W.[Jian-Wei], Sun, T.T.[Tian-Tian], Cao, F.[Feilong],
Image super-resolution via adaptive sparse representation and self-learning,
IET-CV(12), No. 5, August 2018, pp. 753-761.
DOI Link 1807
BibRef

Tan, Z.Y.[Zhen-Yu], Yue, P.[Peng], Di, L.P.[Li-Ping], Tang, J.M.[Jun-Mei],
Deriving High Spatiotemporal Remote Sensing Images Using Deep Convolutional Network,
RS(10), No. 7, 2018, pp. xx-yy.
DOI Link 1808
From High Temporal, Low Spatial resolution. BibRef

Wen, R.[Ran], Fu, K.[Kun], Sun, H.[Hao], Sun, X.[Xian], Wang, L.[Lei],
Image Superresolution Using Densely Connected Residual Networks,
SPLetters(25), No. 10, October 2018, pp. 1565-1569.
IEEE DOI 1810
image resolution, learning (artificial intelligence), neural nets, densely connected residual networks, residual learning BibRef

Lin, G.M.[Gui-Min], Wu, Q.X.[Qing-Xiang], Chen, L.[Liang], Qiu, L.[Lida], Wang, X.[Xuan], Liu, T.J.[Tian-Jian], Chen, X.[Xiyao],
Deep unsupervised learning for image super-resolution with generative adversarial network,
SP:IC(68), 2018, pp. 88-100.
Elsevier DOI 1810
Super-resolution, Deep unsupervised learning, Sub-pixel convolution, Regularizer, Generative adversarial network BibRef

Zheng, Y.[Yan], Cao, X.[Xiang], Xiao, Y.[Yi], Zhu, X.[Xianyi], Yuan, J.[Jin],
Joint residual pyramid for joint image super-resolution,
JVCIR(58), 2019, pp. 53-62.
Elsevier DOI 1901
Deep learning, Neural convolutional pyramid, Joint super-resolution, Residual block BibRef

Arun, P.V.[Pattathal V.], Herrmann, I.[Ittai], Budhiraju, K.M.[Krishna M.], Karnieli, A.[Arnon],
Convolutional network architectures for super-resolution/sub-pixel mapping of drone-derived images,
PR(88), 2019, pp. 431-446.
Elsevier DOI 1901
Sub-pixel mapping, Super-resolution, Convolutional neural network, Class distribution, Drone, UAV BibRef

Xie, W.Y.[Wei-Ying], Shi, Y.Z.[Yan-Zi], Li, Y.S.[Yun-Song], Jia, X.P.[Xiu-Ping], Lei, J.[Jie],
High-quality spectral-spatial reconstruction using saliency detection and deep feature enhancement,
PR(88), 2019, pp. 139-152.
Elsevier DOI 1901
Hyperspectral image, Quality enhancement, Structure tensor, Deep neural networks, Adaptive weighting, Nonnegative matrix factorization BibRef

Chen, Y.X.[Yan-Xiang], Tan, H.[Huadong], Zhang, L.[Luming], Zhou, J.[Jie], Lu, Q.A.[Qi-Ang],
Hybrid image super-resolution using perceptual similarity from pre-trained network,
JVCIR(60), 2019, pp. 229-235.
Elsevier DOI 1903
Super-resolution, Hybrid method, Adaptive weight, Pre-trained VGG network BibRef

Yang, W., Xia, S., Liu, J., Guo, Z.,
Reference-Guided Deep Super-Resolution via Manifold Localized External Compensation,
CirSysVideo(29), No. 5, May 2019, pp. 1270-1283.
IEEE DOI 1905
Manifolds, Image resolution, Image reconstruction, Estimation, Databases, Semantics, Face, Super-resolution, manifold localization, internal structure inference BibRef

Xu, J., Li, M., Fan, J., Zhao, X., Chang, Z.,
Self-Learning Super-Resolution Using Convolutional Principal Component Analysis and Random Matching,
MultMed(21), No. 5, May 2019, pp. 1108-1121.
IEEE DOI 1905
convolution, feature extraction, image matching, image reconstruction, image resolution, principal component analysis BibRef

Guo, D.[Dan], Niu, Y.X.[Yan-Xiong], Xie, P.Y.[Peng-Yan],
Speedy and accurate image super-resolution via deeply recursive CNN with skip connection and network in network,
IET-IPR(13), No. 7, 30 May 2019, pp. 1201-1209.
DOI Link 1906
BibRef

He, Z.W.[Ze-Wei], Tang, S.L.[Si-Liang], Yang, J.X.[Jiang-Xin], Cao, Y.L.[Yan-Long], Yang, M.Y.[Michael Ying], Cao, Y.P.[Yan-Peng],
Cascaded Deep Networks With Multiple Receptive Fields for Infrared Image Super-Resolution,
CirSysVideo(29), No. 8, August 2019, pp. 2310-2322.
IEEE DOI 1908
Image reconstruction, Spatial resolution, Image restoration, Dictionaries, Machine learning, Training, Infrared imaging, receptive fields BibRef

Guo, T., Seyed Mousavi, H., Monga, V.,
Adaptive Transform Domain Image Super-Resolution via Orthogonally Regularized Deep Networks,
IP(28), No. 9, Sep. 2019, pp. 4685-4700.
IEEE DOI 1908
convolutional neural nets, discrete cosine transforms, image resolution, interpolation, complexity constraint BibRef

Jiang, K., Wang, Z., Yi, P., Wang, G., Lu, T., Jiang, J.,
Edge-Enhanced GAN for Remote Sensing Image Superresolution,
GeoRS(57), No. 8, August 2019, pp. 5799-5812.
IEEE DOI 1908
edge detection, feature extraction, gallium compounds, geophysical image processing, image enhancement, superresolution. BibRef


Kim, S.Y.[Soo Ye], Kim, M.C.[Mun-Churl],
A Multi-purpose Convolutional Neural Network for Simultaneous Super-Resolution and High Dynamic Range Image Reconstruction,
ACCV18(III:379-394).
Springer DOI 1906
BibRef

Zhao, Z.Q.[Zhong-Qiu], Hu, J.[Jian], Tian, W.D.[Wei-Dong], Ling, N.[Ning],
Cooperative Adversarial Network for Accurate Super Resolution,
ACCV18(II:98-114).
Springer DOI 1906
BibRef

Liu, J.[Jie], Jung, C.[Cheolkon],
Multiple Connected Residual Network for Image Enhancement on Smartphones,
PerceptualRest18(V:182-196).
Springer DOI 1905
BibRef

Hui, Z.[Zheng], Wang, X.[Xiumei], Deng, L.[Lirui], Gao, X.[Xinbo],
Perception-Preserving Convolutional Networks for Image Enhancement on Smartphones,
PerceptualRest18(V:197-213).
Springer DOI 1905
BibRef

Cheon, M.[Manri], Kim, J.H.[Jun-Hyuk], Choi, J.H.[Jun-Ho], Lee, J.S.[Jong-Seok],
Generative Adversarial Network-Based Image Super-Resolution Using Perceptual Content Losses,
PerceptualRest18(V:51-62).
Springer DOI 1905
BibRef

Kim, J., Lee, J.,
Deep Residual Network with Enhanced Upscaling Module for Super-Resolution,
Restoration18(913-9138)
IEEE DOI 1812
Convolution, Image resolution, Feature extraction, Training, Image reconstruction, Computer vision, Signal resolution BibRef

Tan, W., Yan, B., Bare, B.,
Feature Super-Resolution: Make Machine See More Clearly,
CVPR18(3994-4002)
IEEE DOI 1812
Image resolution, Feature extraction, Generative adversarial networks, Data models, Image recognition, Euclidean distance BibRef

Gao, P., Xue, J., Lu, K., Yan, Y.,
A fast Cascade Shape Regression Method based on CNN-based Initialization,
ICPR18(3037-3042)
IEEE DOI 1812
Shape, Face, Training, Interpolation, Convolution, Neural networks, Splines (mathematics) BibRef

Jiang, T., Wu, X., Yu, Z., Shui, W., Lu, G., Guo, S., Fei, H., Zhang, Q.,
Recursive Inception Network for Super-Resolution,
ICPR18(2759-2764)
IEEE DOI 1812
Feature extraction, Training, Image reconstruction, Image resolution, Periodic structures, Interpolation, Convolution BibRef

Kasem, H.M., Hung, K., Jiang, J.,
Revised Spatial Transformer Network towards Improved Image Super-resolutions,
ICPR18(2688-2692)
IEEE DOI 1812
Spatial resolution, Signal resolution, Image reconstruction, Training, Transforms, Interpolation BibRef

Gao, H., Chen, Z., Ma, G., Xie, W., Li, Z.,
Deep Pixel Probabilistic Model for Super Resolution Based on Human Visual Saliency Mechanism,
ICPR18(2747-2752)
IEEE DOI 1812
Training, Image resolution, Probabilistic logic, Visualization, Interpolation, Computational modeling, Optimization, image quality assessment BibRef

Zhang, Y.L.[Yu-Lun], Tian, Y., Kong, Y., Zhong, B.N.[Bi-Neng], Fu, Y.[Yun],
Residual Dense Network for Image Super-Resolution,
CVPR18(2472-2481)
IEEE DOI 1812
Feature extraction, Convolution, Image resolution, Computer vision, Training, Buildings, Fuses BibRef

Zhang, Y.L.[Yu-Lun], Li, K.P.[Kun-Peng], Li, K.[Kai], Wang, L.C.[Li-Chen], Zhong, B.N.[Bi-Neng], Fu, Y.[Yun],
Image Super-Resolution Using Very Deep Residual Channel Attention Networks,
ECCV18(VII: 294-310).
Springer DOI 1810
BibRef

Bulat, A.[Adrian], Yang, J.[Jing], Tzimiropoulos, G.[Georgios],
To Learn Image Super-Resolution, Use a GAN to Learn How to Do Image Degradation First,
ECCV18(VI: 187-202).
Springer DOI 1810
BibRef

Liu, Z.S.[Zhi-Song], Wan-Chi, S.[Siu],
Cascaded Random Forests for Fast Image Super-Resolution,
ICIP18(2531-2535)
IEEE DOI 1809
Image resolution, Decision trees, Mathematical model, Feature extraction, Image reconstruction, Signal resolution, machine learning BibRef

Liling, Z., Zelin, Z., Quansen, S.,
Deep Learning Based Super Resolution Using Significant and General Regions,
ICIP18(2516-2520)
IEEE DOI 1809
Training, Image resolution, Image reconstruction, Feature extraction, Roads, Machine learning, Big Data, deep learning, significant regions BibRef

Sugawara, Y., Shiota, S., Kiya, H.,
Super-Resolution Using Convolutional Neural Networks Without Any Checkerboard Artifacts,
ICIP18(66-70)
IEEE DOI 1809
Convolution, Deconvolution, Training, Linear systems, Steady-state, Image resolution, Kernel, Super-Resolution, Checkerboard Artifacts BibRef

Xu, J., Chae, Y., Stenger, B., Datta, A.,
Dense Bynet: Residual Dense Network for Image Super Resolution,
ICIP18(71-75)
IEEE DOI 1809
Convolutional codes, Training, Spatial resolution, Task analysis, Benchmark testing, Network architecture, image super resolution, image enhancement BibRef

Wang, Q., Fan, H., Cong, Y., Tang, Y.,
Large receptive field convolutional neural network for image super-resolution,
ICIP17(958-962)
IEEE DOI 1803
Convolution, Convolutional neural networks, Feature extraction, Kernel, Spatial resolution, Training, Convolutional neural network, Super resolution BibRef

Liu, Y., Chen, Q., Wassell, I.,
Deep network for image super-resolution with a dictionary learning layer,
ICIP17(967-971)
IEEE DOI 1803
Computer architecture, DH-HEMTs, Dictionaries, Encoding, Image reconstruction, Image resolution, Machine learning, Super-resolution BibRef

Xu, J., Chae, Y., Stenger, B.,
BYNET-SR: Image super resolution with a bypass connection network,
ICIP17(4053-4057)
IEEE DOI 1803
Convergence, Convolution, Image resolution, Mathematical model, Signal resolution, Task analysis, Training, super resolution BibRef

Fan, R., Li, S., Lei, G., Yue, G.,
Shallow and deep convolutional networks for image super-resolution,
ICIP17(1847-1851)
IEEE DOI 1803
Convolution, Feature extraction, Image resolution, Image restoration, Interpolation, Training, Videos, Super-resolution, multi-scale manner BibRef

Bao, W.B.[Wen-Bo], Zhang, X.Y.[Xiao-Yun], Yan, S.P.[Shang-Peng], Gao, Z.Y.[Zhi-Yong],
Iterative convolutional neural network for noisy image super-resolution,
ICIP17(4038-4042)
IEEE DOI 1803
Convolutional neural networks, Image reconstruction, Noise measurement, Noise reduction, Spatial resolution, Training, super-resolution BibRef

Zhou, L.[Liguo], Wang, Z.Y.[Zhong-Yuan], Wang, S.[Shu], Luo, Y.M.[Yi-Min],
Coarse-to-Fine Image Super-Resolution Using Convolutional Neural Networks,
MMMod18(II:73-81).
Springer DOI 1802
BibRef

Lai, W.S., Huang, J.B., Ahuja, N., Yang, M.H.,
Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution,
CVPR17(5835-5843)
IEEE DOI 1711
Convolution, Feature extraction, Image reconstruction, Laplace equations, Spatial resolution, Training BibRef

Ren, H., El-Khamy, M., Lee, J.,
CT-SRCNN: Cascade Trained and Trimmed Deep Convolutional Neural Networks for Image Super Resolution,
WACV18(1423-1431)
IEEE DOI 1806
BibRef
Earlier:
Image Super Resolution Based on Fusing Multiple Convolution Neural Networks,
NTIRE17(1050-1057)
IEEE DOI 1709
feedforward neural nets, image resolution, learning (artificial intelligence), CT-SRCNN, SR efficiency, Tuning. Convolution, Kernel, Neural networks, Training. BibRef

Cui, Z.[Zhen], Chang, H.[Hong], Shan, S.G.[Shi-Guang], Zhong, B.[Bineng], Chen, X.L.[Xi-Lin],
Deep Network Cascade for Image Super-resolution,
ECCV14(V: 49-64).
Springer DOI 1408
BibRef

Ma, L.[Lin], Zhang, Y.H.[Yong-Hua], Lu, Y.[Yan], Wu, F.[Feng], Zhao, D.B.[De-Bin],
Three-tiered network model for image hallucination,
ICIP08(357-360).
IEEE DOI 0810
BibRef

Chapter on Motion Analysis -- Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion continues in
Registration for Super Resolution .


Last update:Sep 9, 2019 at 16:26:55