메뉴 건너뛰기




Volumn 07-12-June-2015, Issue , 2015, Pages 3791-3799

Fast and accurate image upscaling with super-resolution forests

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL EFFICIENCY; DECISION TREES; FACE RECOGNITION; OPTICAL RESOLVING POWER; PATTERN RECOGNITION;

EID: 84959234116     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2015.7299003     Document Type: Conference Paper
Times cited : (700)

References (39)
  • 1
    • 33750383209 scopus 로고    scopus 로고
    • K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation
    • M. Aharon, M. Elad, and A. Bruckstein. K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation. TSP, 54(11):4311-4322, 2006
    • (2006) TSP , vol.54 , Issue.11 , pp. 4311-4322
    • Aharon, M.1    Elad, M.2    Bruckstein, A.3
  • 2
    • 0001492549 scopus 로고    scopus 로고
    • Shape quantization and recognition with randomized trees
    • Y. Amit and D. Geman. Shape Quantization and Recognition with Randomized Trees. NECO, 9(7):1545-1588, 1997
    • (1997) NECO , vol.9 , Issue.7 , pp. 1545-1588
    • Amit, Y.1    Geman, D.2
  • 3
    • 79953048649 scopus 로고    scopus 로고
    • Contour detection and hierarchical image segmentation
    • P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik. Contour Detection and Hierarchical Image Segmentation. PAMI, 33(5):898-916, 2011
    • (2011) PAMI , vol.33 , Issue.5 , pp. 898-916
    • Arbelaez, P.1    Maire, M.2    Fowlkes, C.3    Malik, J.4
  • 4
    • 84898409537 scopus 로고    scopus 로고
    • Low-complexity single-image super-resolution based on nonnegative neighbor embedding
    • M. Bevilacqua, A. Roumy, C. Guillemot, and M.-L. Alberi Morel. Low-Complexity Single-Image Super-Resolution based on Nonnegative Neighbor Embedding. In BMVC, 2012
    • (2012) BMVC
    • Bevilacqua, M.1    Roumy, A.2    Guillemot, C.3    Alberi Morel, M.-L.4
  • 6
    • 0035478854 scopus 로고    scopus 로고
    • Random Forests
    • L. Breiman. Random Forests. ML, 45(1):5-32, 2001
    • (2001) ML , vol.45 , Issue.1 , pp. 5-32
    • Breiman, L.1
  • 7
    • 56449089785 scopus 로고    scopus 로고
    • An empirical evaluation of supervised learning in high dimensions
    • R. Caruana, N. Karampatziakis, and A. Yessenalina. An empirical Evaluation of Supervised Learning in High Dimensions. In ICML, 2008
    • (2008) ICML
    • Caruana, R.1    Karampatziakis, N.2    Yessenalina, A.3
  • 8
    • 5044219639 scopus 로고    scopus 로고
    • Super-resolution through neighbor embedding
    • H. Chang, D.-Y. Yeung, and Y. Xiong. Super-Resolution Through Neighbor Embedding. In CVPR, 2004
    • (2004) CVPR
    • Chang, H.1    Yeung, D.-Y.2    Xiong, Y.3
  • 9
    • 84878866110 scopus 로고    scopus 로고
    • Robust and accurate shape model fitting using random forest regression voting
    • T. F. Cootes, M. Ionita, C. Lindner, and P. Sauer. Robust and Accurate Shape Model Fitting using Random Forest Regression Voting. In ECCV, 2012
    • (2012) ECCV
    • Cootes, T.F.1    Ionita, M.2    Lindner, C.3    Sauer, P.4
  • 11
    • 84959234961 scopus 로고    scopus 로고
    • Jointly optimized regressors for image super-resolution
    • D. Dai, R. Timofte, and L. van Gool. Jointly Optimized Regressors for Image Super-resolution. In Eurographs, 2015
    • (2015) Eurographs
    • Dai, D.1    Timofte, R.2    Van Gool, L.3
  • 12
    • 84866706750 scopus 로고    scopus 로고
    • Real-time facial feature detection using conditional regression forests
    • M. Dantone, J. Gall, G. Fanelli, and L. v. Gool. Real-time Facial Feature Detection using Conditional Regression Forests. In CVPR, 2012
    • (2012) CVPR
    • Dantone, M.1    Gall, J.2    Fanelli, G.3    Gool, V.L.4
  • 13
    • 84898820142 scopus 로고    scopus 로고
    • Structured forests for fast edge detection
    • P. Dollár and L. Zitnick. Structured Forests for Fast Edge Detection. In ICCV, 2013
    • (2013) ICCV
    • Dollár, P.1    Zitnick, L.2
  • 14
    • 84921971467 scopus 로고    scopus 로고
    • Learning a deep convolutional network for image super-resolution
    • C. Dong, C. Change Loy, K. He, and X. Tang. Learning a deep convolutional network for image super-resolution. In ECCV, 2014
    • (2014) ECCV
    • Dong, C.1    Change Loy, C.2    He, K.3    Tang, X.4
  • 15
    • 0018506597 scopus 로고
    • Lanczos filtering in one and two dimensions
    • C. E. Duchon. Lanczos Filtering in One and Two Dimensions. JAM, 18(8):1016-1022, 1979
    • (1979) JAM , vol.18 , Issue.8 , pp. 1016-1022
    • Duchon, C.E.1
  • 17
    • 84869206350 scopus 로고    scopus 로고
    • Upsampling via imposed edges statistics
    • R. Fattal. Upsampling via Imposed Edges Statistics. TOG, 26(3):95, 2007
    • (2007) TOG , vol.26 , Issue.3 , pp. 95
    • Fattal, R.1
  • 18
    • 0036500772 scopus 로고    scopus 로고
    • Example-based super-resolution
    • W. T. Freeman, T. R. Jones, and E. C. Pasztor. Example-Based Super-Resolution. CGA, 22(2):56-65, 2002
    • (2002) CGA , vol.22 , Issue.2 , pp. 56-65
    • Freeman, W.T.1    Jones, T.R.2    Pasztor, E.C.3
  • 19
    • 70450201402 scopus 로고    scopus 로고
    • Class-specific hough forests for object detection
    • J. Gall and V. Lempitsky. Class-Specific Hough Forests for Object Detection. In CVPR, 2009
    • (2009) CVPR
    • Gall, J.1    Lempitsky, V.2
  • 20
    • 33646430006 scopus 로고    scopus 로고
    • Extremely randomized trees
    • P. Geurts, D. Ernst, and L. Wehenkel. Extremely randomized trees. ML, 63(1):3-42, 2006
    • (2006) ML , vol.63 , Issue.1 , pp. 3-42
    • Geurts, P.1    Ernst, D.2    Wehenkel, L.3
  • 22
    • 77953187337 scopus 로고    scopus 로고
    • Super-resolution from a single image
    • Glasner, Daniel, Bagon, Shai and Irani, Michal. Super-Resolution From a Single Image. In ICCV, 2009
    • (2009) ICCV
    • Glasner, D.1    Bagon, S.2    Irani, M.3
  • 23
    • 84887371128 scopus 로고    scopus 로고
    • Beta process joint dictionary learning for coupled feature spaces with application to single image super-resolution
    • L. He, H. Qi, and R. Zaretzki. Beta Process Joint Dictionary Learning for Coupled Feature Spaces with Application to Single Image Super-Resolution. In CVPR, 2013
    • (2013) CVPR
    • He, L.1    Qi, H.2    Zaretzki, R.3
  • 25
    • 84887354170 scopus 로고    scopus 로고
    • Sketch tokens: A learned mid-level representation for contour and object detection
    • J. J. Lim, C. L. Zitnick, and P. Dollár. Sketch Tokens: A Learned Mid-level Representation for Contour and Object Detection. In CVPR, 2013
    • (2013) CVPR
    • Lim, J.J.1    Zitnick, C.L.2    Dollár, P.3
  • 26
    • 84898792897 scopus 로고    scopus 로고
    • Abnormal event detection at 150 fps in matlab
    • C. Lu, J. Shi, and J. Jia. Abnormal Event Detection at 150 FPS in MATLAB. In ICCV, 2013
    • (2013) ICCV
    • Lu, C.1    Shi, J.2    Jia, J.3
  • 28
    • 84911368266 scopus 로고    scopus 로고
    • Neural Decision Forest for Semantic Image Labelling
    • S. Rota Bulò and P. Kontschieder. Neural Decision Forest for Semantic Image Labelling. In CVPR, 2014
    • (2014) CVPR
    • Rota Bulò, S.1    Kontschieder, P.2
  • 29
    • 84898817145 scopus 로고    scopus 로고
    • Alternating regression forests for object detection and pose estimation
    • S. Schulter, C. Leistner, P. Wohlhart, P. M. Roth, and H. Bischof. Alternating Regression Forests for Object Detection and Pose Estimation. In ICCV, 2013
    • (2013) ICCV
    • Schulter, S.1    Leistner, C.2    Wohlhart, P.3    Roth, P.M.4    Bischof, H.5
  • 31
    • 84932095280 scopus 로고    scopus 로고
    • A+: Adjusted anchored neighborhood regression for fast super-resolution
    • R. Timofte, V. De Smet,, and L. Van Gool. A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution. In ACCV, 2014
    • (2014) ACCV
    • Timofte, R.1    De Smet, V.2    Van Gool, L.3
  • 32
    • 84898792173 scopus 로고    scopus 로고
    • Anchored neighborhood regression for fast example-based super-resolution
    • R. Timofte, V. De Smet, and L. Van Gool. Anchored Neighborhood Regression for Fast Example-Based Super-Resolution. In ICCV, 2013
    • (2013) ICCV
    • Timofte, R.1    De Smet, V.2    Van Gool, L.3
  • 33
    • 33747070740 scopus 로고    scopus 로고
    • Image super-resolution survey
    • J. van Ouwerkerk. Image super-resolution survey. IVC, 24(10):1039-1052, 2006
    • (2006) IVC , vol.24 , Issue.10 , pp. 1039-1052
    • Van Ouwerkerk, J.1
  • 34
    • 0022026217 scopus 로고
    • Random Sampling with a Reservoir
    • J. S. Vitter. Random Sampling with a Reservoir. TOMS, 11(1):37-57, 1985
    • (1985) TOMS , vol.11 , Issue.1 , pp. 37-57
    • Vitter, J.S.1
  • 35
    • 84866652383 scopus 로고    scopus 로고
    • Semi-Coupled Dictionary Learning with Applications to Image Super-Resolution and Photo-Sketch Synthesis
    • S. Wang, L. Zhang, Y. Liang, and Q. Pan. Semi-Coupled Dictionary Learning with Applications to Image Super-Resolution and Photo-Sketch Synthesis. In CVPR, 2012
    • (2012) CVPR
    • Wang, S.1    Zhang, L.2    Liang, Y.3    Pan, Q.4
  • 36
    • 84955298423 scopus 로고    scopus 로고
    • Single-image super-resolution: A benchmark
    • C.-Y. Yang, C. Ma, and M.-H. Yang. Single-Image Super-Resolution: A Benchmark. In ECCV, 2014
    • (2014) ECCV
    • Yang, C.-Y.1    Ma, C.2    Yang, M.-H.3
  • 37
    • 84898787395 scopus 로고    scopus 로고
    • Fast direct super-resolution by simple functions
    • C.-Y. Yang and M.-H. Yang. Fast Direct Super-Resolution by Simple Functions. In ICCV, 2013
    • (2013) ICCV
    • Yang, C.-Y.1    Yang, M.-H.2
  • 38
    • 78049312324 scopus 로고    scopus 로고
    • Image super-resolution via sparse representation
    • J. Yang, J. Wright, T. Huang, and Y. Ma. Image Super-Resolution Via Sparse Representation. TIP, 19(11):2861-2873, 2010
    • (2010) TIP , vol.19 , Issue.11 , pp. 2861-2873
    • Yang, J.1    Wright, J.2    Huang, T.3    Ma, Y.4
  • 39
    • 80052803206 scopus 로고    scopus 로고
    • On single image scale-up using sparse-representations
    • R. Zeyde, M. Elad, and M. Protter. On Single Image Scale-Up using Sparse-Representations. In Curves and Surfaces, 2010.
    • (2010) Curves and Surfaces
    • Zeyde, R.1    Elad, M.2    Protter, M.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.