Fast image interpolation via random forests

IEEE Transactions on Image Processing

Volumn 24, Issue 10, 2015, Pages 3232-3245

  Huang, Jun Jie ^a   Siu, Wan Chi ^a   Liu, Tian Rui ^a,b  

^a HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

^b UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

classification; decision trees; image interpolation; Image processing; random forests; scalable interpolation; training and linear regression

Indexed keywords

CLASSIFICATION (OF INFORMATION); FORESTRY; IMAGE CODING; IMAGE PROCESSING; INTERPOLATION; LINEAR REGRESSION; REGRESSION ANALYSIS; SIGNAL TO NOISE RATIO;

AUTO REGRESSIVE MODELS; COMPUTATIONALLY-SCALABLE; HIGH RESOLUTION IMAGE; IMAGE INTERPOLATIONS; LINEAR REGRESSION MODELS; LOW RESOLUTION IMAGES; PEAK SIGNAL TO NOISE RATIO; RANDOM FORESTS;

DECISION TREES;

FORESTS; MODELS; REGRESSION ANALYSIS;

EID: 84933530044     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2015.2440751     Document Type: Article

References (46)

1. W. T. Freeman, E. C. Pasztor, and O. T. Carmichael, "Learning low-level vision," Int. J. Comput. Vis., vol. 40, no. 1, pp. 25-47, 2000.
2. W. T. Freeman, T. R. Jones, and E. C. Pasztor, "Example-based superresolution," IEEE Comput. Graph. Appl., vol. 22, no. 2, pp. 56-65, Mar./Apr. 2002.
3. D. Glasner, S. Bagon, and M. Irani, "Super-resolution from a single image," in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), Kyoto, Japan, Sep./Oct. 2009, pp. 349-356.
4. G. Freedman and R. Fattal, "Image and video upscaling from local self-examples," ACM Trans. Graph., vol. 30, no. 2, 2011, Art. ID 12.
5. J. Yang, J. Wright, T. S. Huang, and Y. Ma, "Image super-resolution via sparse representation," IEEE Trans. Image Process., vol. 19, no. 11, pp. 2861-2873, Nov. 2010.
6. K. I. Kim and Y. Kwon, "Single-image super-resolution using sparse regression and natural image prior," IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 6, pp. 1127-1133, Jun. 2010.
7. W. Dong, D. Zhang, G. Shi, and X. Wu, "Image deblurring and superresolution by adaptive sparse domain selection and adaptive regularization," IEEE Trans. Image Process., vol. 20, no. 7, pp. 1838-1857, Jul. 2011.
8. X. Li, K. M. Lam, G. Qiu, L. Shen, and S. Wang, "An efficient example-based approach for image super-resolution," in Proc. IEEE Int. Conf. Neural Netw. Signal Process., Nanjing, China, Jun. 2008, pp. 575-580.
9. S. Gu, N. Sang, and F. Ma, "Fast image super resolution via local regression," in Proc. IEEE 21st Int. Conf. Pattern Recognit. (ICPR), Tsukuba, Japan, Nov. 2012, pp. 3128-3131.
10. C.-Y. Yang and M.-H. Yang, "Fast direct super-resolution by simple functions," in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), Sydney, NSW, Australia, Dec. 2013, pp. 561-568.
11. K. S. Ni and T. Q. Nguyen, "Image superresolution using support vector regression," IEEE. Trans. Image Process., vol. 16, no. 6, pp. 1596-1610, Jun. 2007.
12. R. Keys, "Cubic convolution interpolation for digital image processing," IEEE Trans. Acoust., Speech, Signal Process., vol. 29, no. 6, pp. 1153-1160, Dec. 1981.
13. H. S. Hou and H. Andrews, "Cubic splines for image interpolation and digital filtering," IEEE Trans. Acoust., Speech, Signal Process., vol. 26, no. 6, pp. 508-517, Dec. 1978.
14. T. Blu, P. Thevenaz, and M. Unser, "Linear interpolation revitalized," IEEE Trans. Image Process., vol. 13, no. 6, pp. 710-719, May 2004.
15. T. M. Lehmann, C. Gönner, and K. Spitzer, "Addendum: B-spline interpolation in medical image processing," IEEE Trans. Med. Imag., vol. 20, no. 7, pp. 660-665, Jul. 2001.
16. K. Jensen and D. Anastassiou, "Subpixel edge localization and the interpolation of still images," IEEE Trans. Image Process., vol. 4, no. 3, pp. 285-295, Mar. 1995.
17. H. Shi and R. Ward, "Canny edge based image expansion," in Proc. IEEE Int. Symp. Circuits Syst., vol. 1. May 2002, pp. I-785-I-788.
18. Q. Wang and R. K. Ward, "A new orientation-adaptive interpolation method," IEEE Trans. Image Process., vol. 16, no. 4, pp. 889-900, Apr. 2007.
19. D. D. Muresan, "Fast edge directed polynomial interpolation," in Proc. IEEE Int. Conf. Image Process. (ICIP), vol. 2. Genova, Italy, Sep. 2005, pp. II-990-II-993.
20. X. Li and M. T. Orchard, "New edge-directed interpolation," IEEE Trans. Image Process., vol. 10, no. 10, pp. 1521-1527, Oct. 2001.
21. W.-S. Tam, C.-W. Kok, and W.-C. Siu, "Modified edge-directed interpolation for images," J. Electron. Imag., vol. 19, no. 1, pp. 013011-1-013011-20, Jan./Mar. 2010.
22. C.-S. Wong and W.-C. Siu, "Further improved edge-directed interpolation and fast EDI for SDTV to HDTV conversion," in Proc. 18th Eur. Signal Process. Conf. (EUSIPCO), Aalborg, Denmark, Aug. 2010, pp. 309-313.
23. C.-S. Wong and W.-C. Siu, "Adaptive directional window selection for edge-directed interpolation," in Proc. 19th Int. Conf. Comput. Commun. Netw. (ICCCN), Zürich, Switzerland, Aug. 2010, pp. 1-6.
24. L. Zhang and X. Wu, "An edge-guided image interpolation algorithm via directional filtering and data fusion," IEEE Trans. Image Process., vol. 15, no. 8, pp. 2226-2238, Aug. 2006.
25. X. Zhang and X. Wu, "Image interpolation by adaptive 2D autoregressive modeling and soft-decision estimation," IEEE Trans. Image Process., vol. 17, no. 6, pp. 887-896, Jun. 2008.
26. K.-W. Hung and W.-C. Siu, "Fast image interpolation using the bilateral filter," IET Image Process., vol. 6, no. 7, pp. 877-890, Oct. 2012.
27. K.-W. Hung and W.-C. Siu, "Robust soft-decision interpolation using weighted least squares," IEEE Trans. Image Process., vol. 21, no. 3, pp. 1061-1069, Mar. 2012.
28. X. Liu, D. Zhao, R. Xiong, S. Ma, W. Gao, and H. Sun, "Image interpolation via regularized local linear regression," IEEE Trans. Image Process., vol. 20, no. 12, pp. 3455-3469, Dec. 2011.
29. A. Giachetti and N. Asuni, "Real-time artifact-free image upscaling," IEEE Trans. Image Process., vol. 20, no. 10, pp. 2760-2768, Oct. 2011.
30. H. Kirshner, A. Bourquard, J. P. Ward, M. Porat, and M. Unser, "Adaptive image resizing based on continuous-domain stochastic modeling," IEEE Trans. Image Process., vol. 23, no. 1, pp. 413-423, Jan. 2014.
31. K. S. Ni and T. Q. Nguyen, "An adaptable k-nearest neighbors algorithm for MMSE image interpolation," IEEE Trans. Image Process., vol. 18, no. 9, pp. 1976-1987, Sep. 2009.
32. Y. Romano, M. Protter, and M. Elad, "Single image interpolation via adaptive nonlocal sparsity-based modeling," IEEE Trans. Image Process., vol. 23, no. 7, pp. 3085-3098, May 2014.
33. W. Dong, L. Zhang, R. Lukac, and G. Shi, "Sparse representation based image interpolation with nonlocal autoregressive modeling," IEEE Trans. Image Process., vol. 22, no. 4, pp. 1382-1394, Apr. 2013.
34. J. Gall, A. Yao, N. Razavi, L. Van Gool, and V. Lempitsky, "Hough forests for object detection, tracking, and action recognition," IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 11, pp. 2188-2202, Nov. 2011.
35. V. Lepetit and P. Fua, "Keypoint recognition using randomized trees," IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 9, pp. 1465-1479, Sep. 2006.
36. P. Dollár and C. L. Zitnick, "Structured forests for fast edge detection," in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), Sydney, NSW, Australia, Dec. 2013, pp. 1841-1848.
37. M. Dantone, J. Gall, G. Fanelli, and L. Van Gool, "Real-time facial feature detection using conditional regression forests," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Providence, RI, USA, Jun. 2012, pp. 2578-2585.
38. A. Bosch, A. Zisserman, and X. Muoz, "Image classification using random forests and ferns," in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), Rio de Janeiro, Brazil, Oct. 2007, pp. 1-8.
39. J. Shotton, M. Johnson, and R. Cipolla, "Semantic texton forests for image categorization and segmentation," in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit. (CVPR), Anchorage, AK, USA, Jun. 2008, pp. 1-8.
40. L. Breiman, "Random forests," Mach. Learn., vol. 45, no. 1, pp. 5-32, 2001.
41. P. Geurts, D. Ernst, and L. Wehenkel, "Extremely randomized trees," Mach. Learn., vol. 63, no. 1, pp. 3-42, 2006.
42. L. Breiman, J. Friedman, C. J. Stone, and R. A. Olshen, Classification and Regression Trees. Boca Raton, FL, USA: CRC Press, 1984.
43. A. Criminisi, J. Shotton, and E. Konukoglu, "Decision forests: A unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning," Found. Trends Comput. Graph. Vis., vol. 7, nos. 2-3, pp. 81-227, 2012.
44. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: From error visibility to structural similarity," IEEE Trans. Image Process., vol. 13, no. 4, pp. 600-612, Apr. 2004.
45. L. Zhang, L. Zhang, X. Mou, and D. Zhang, "FSIM: A feature similarity index for image quality assessment," IEEE Trans. Image Process., vol. 20, no. 8, pp. 2378-2386, Aug. 2011.
46. Fast Image Interpolation via Random Forests. [Online]. Available: http://www.eie.polyu.edu.hk/~wcsiu/softmodule/10/ fast-image-interpolation.htm, accessed Jun. 10, 2015.