Resolution enhancement of textual images via multiple coupled dictionaries and adaptive sparse representation selection

International Journal on Document Analysis and Recognition

Volumn 18, Issue 1, 2015, Pages 87-107

  Walha, Rim ^a   Drira, Fadoua ^a   Lebourgeois, Frank ^b   Garcia, Christophe ^b   Alimi, Adel M ^a  

^a UNIVERSITY OF SFAX   (Tunisia)

^b UNIVERSITÉ DE LYON   (France)

Author keywords

Character recognition; Histogram of structure tensors; Resolution enhancement; Sparse coding; Textual image; Writing pattern

Indexed keywords

CHARACTER RECOGNITION; DATABASE SYSTEMS; IMAGE CODING; OPTICAL CHARACTER RECOGNITION;

COMPARATIVE STUDIES; HIGH QUALITY IMAGES; LOCAL FEATURE DESCRIPTOR; RESOLUTION ENHANCEMENT; SPARSE CODING; SPARSE REPRESENTATION; STRUCTURE TENSORS; TEXTUAL IMAGE;

IMAGE ENHANCEMENT;

EID: 84925488085     PISSN: 14332833     EISSN: 14332825     Source Type: Journal    
DOI: 10.1007/s10032-014-0235-6     Document Type: Article

References (66)

1. Aharon, M., Elad, M., Bruckstein, A.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54(11), 4311–4322 (2006)
2. Alvarez, L., Lions, P.L., Morel, J.M.: Image selective smoothing and edge detection by nonlinear diffusion. ii. SIAM J. Numer. Anal. 29(3), 845–866 (1992)
3. Arbelaitz, O., Gurrutxaga, I., Muguerza, J., PéRez, J.M., Perona, I.: An extensive comparative study of cluster validity indices. Pattern Recogn. 46(1), 243–256 (2013)
4. Banerjee, J., Namboodiri, A.M., Jawahar, C.V.: Contextual restoration of severely degraded document images. In: CVPR, pp. 517–524, IEEE (2009)
5. Batagelj, V., Bren, M.: Comparing resemblance measures. J. Classif. 12(1), 73–90 (1995)
6. Ben-Ezra, M., Lin, Z., Wilburn, B.: Penrose pixels super-resolution in the detector layout domain. In: ICCV, pp. 1–8. IEEE (2007)
7. Bern, M.W., Goldberg, D.: Scanner-model-based document image improvement. In: ICIP, pp. 582–585 (2000)
8. Caliński, T., Harabasz, J.: A dendrite method for cluster analysis. Commun. Stat. Simul. Comput. 3(1), 1–27 (1974)
9. Caner, G., Haritaoglu, I.: Shape-dna: effective character restoration and enhancement for arabic text documents. In: Proceedings of ICPR, pp. 2053–2056. IEEE Computer Society, Washington, DC, USA (2010)
10. Chang, H., Yeung, D.Y., Xiong, Y.: Super-resolution through neighbor embedding. In: CVPR (1), pp. 275–282 (2004)
11. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR (1), pp. 886–893 (2005)
12. Dalley, G., Freeman, W.T., Marks, J.: Single-frame text super-resolution: a Bayesian approach. In: ICIP, pp. 3295–3298. IEEE (2004)
13. Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. 1(2), 224–227 (1979)
14. Di Zenzo, S.: A note on the gradient of a multi-image. Comput. Vis. Graph. Image Process. 33(1), 116–125 (1986)
15. Dong, W., Zhang, L., Lukac, R., Shi, G.: Sparse representation based image interpolation with nonlocal autoregressive modeling. IEEE Trans. Image Process. 22(4), 1382–1394 (2013)
16. Drira, F., Lebourgeois, F., Emptoz, H.: Document images restoration by a new tensor based diffusion process: application to the recognition of old printed documents. In: ICDAR, pp. 321–325. IEEE Computer Society (2009)
17. Drira, F., Lebourgeois, F., Emptoz, H.: A new pde-based approach for singularity-preserving regularization: application to degraded characters restoration. IJDAR 15(3), 183–212 (2012)
18. Einsele, F., Ingold, R.: A study of the variability of very low resolution characters and the feasibility of their discrimination using geometrical features. In: Proceedings of International Conference on Pattern Recognition and Computer Vision, pp. 213–217, Istanbul (Turkey) (2005)
19. Einsele, F., Ingold, R., Hennebert, J.: A language-independent, open-vocabulary system based on hmms for recognition of ultra low resolution words. In: Proceedings of ACM SAC, Fortaleza (Brasil) (2008)
20. Fadili, M.J., Starck, J.L.: Em algorithm for sparse representation-based image inpainting. In: ICIP (2), pp. 61–64. IEEE (2005)
21. Fan, W., 0004, J.S., Naoi, S., Minagawa, A., Hotta, Y.: Local consistency constrained adaptive neighbor embedding for text image super-resolution. In: DAS, pp. 90–94. IEEE (2012)
22. Freeman, G., Fattal, R.: Image and video upscaling from local self-examples. ACM Trans. Graph. 28(3), 1–10 (2010)
23. Hoang, T.V., Smith, E.H.B., Tabbone, S.: Sparsity-based edge noise removal from bilevel graphical document images. IJDAR 17(2), 161–179 (2014)
24. Hobby, J.D., Ho, T.K.: Enhancing degraded document images via bitmap clustering and averaging. In: Proceedings of ICDAR, pp. 394–400. Washington, DC (1997)
25. Keys, R.: Cubic convolution interpolation for digital image processing. IEEE Trans. Acoust. Speech Signal Process. 29(6), 1153–1160 (1981)
26. Kim, H.Y.: Binary operator design by k-nearest neighbor learning with application to image resolution increasing. Int. J. Imaging Syst. Technol. 11(5), 331–339 (2000)
27. Kthe, U.: Edge and junction detection with an improved structure tensor. In: Krell, G. (ed.) Pattern Recognition. Proceedings of 25th DAGM Symposium, Springer LNCS, vol. 2781, pp. 25–32. Springer (2003)
28. Kumar, V., Bansal, A., Tulsiyan, G.H., Mishra, A., Namboodiri, A.M., Jawahar, C.V.: Sparse document image coding for restoration. In: ICDAR, pp. 713–717 (2013)
29. Lee, H., Battle, A., Raina, R., Ng, A.Y.: Efficient sparse coding algorithms. In: NIPS, pp. 801–808. NIPS (2007)
30. Li, X., Orchard, M.T.: New edge-directed interpolation. IEEE Trans. Image Process. 10(10), 1521–1527 (2001)
31. Liang, J., Doermann, D.S., Li, H.: Camera-based analysis of text and documents: a survey. IJDAR 7(2–3), 84–104 (2005)
32. Lopresti, D.P., Zhou, J., Nagy, G., Sarkar, P.: Spatial sampling effects in optical character recognition. In: ICDAR, pp. 309–314. IEEE Computer Society (1995)
33. Lukin, A., Krylov, A., Nasonov, A.: Image interpolation by super-resolution. In: 16th International Conference Graphicon’2006, pp. 239–242 (2006)
34. Luong, H., Philips, W.: Non-local text image reconstruction. In: Proceedings of ICDAR, vol. 1, pp. 546–550. Curitiba, Brazil (2007)
35. Luong, H.Q., Philips, W.: Robust reconstruction of low-resolution document images by exploiting repetitive character behaviour. IJDAR 11(1), 39–51 (2008)
36. Mairal, J., Mairal, J., Elad, M., Elad, M., Sapiro, G., Sapiro, G.: Sparse representation for color image restoration. IEEE Trans. Image Process. 17(1), 53–69 (2007)
37. Minetto, R., Thome, N., Cord, M., Leite, N.J., Stolfi, J.: T-hog: an effective gradient-based descriptor for single line text regions. Pattern Recogn. 46(3), 1078–1090 (2013)
38. Mirkin, B.: Clustering for Data Mining: A Data Recovery Approach (Chapman & Hall/CRC Computer Science & Data Analysis), 1st edn. Chapman and Hall, London (2005)
39. Namane, A.: Sid-Ahmed: character scaling by contour method. IEEE Trans. Pattern Anal. Mach. Intell. 12(6), 600–606 (1990)
40. Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29(1), 51–59 (1996)
41. Park, J., Kwon, Y., Kim, J.H.: An example-based prior model for text image super-resolution. In: ICDAR, pp. 374–378. IEEE Computer Society (2005)
42. Rashid, S.F., Shafait, F., Breuel, T.M.: An evaluation of hmm-based techniques for the recognition of screen rendered text. In: ICDAR, pp. 1260–1264. IEEE (2011)
43. Rice, S.V.: Measuring the accuracy of page-reading systems. Ph.D. thesis (1996)
44. Rousseeuw, P.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20(1), 53–65 (1987)
45. Rowley-Brooke, R., Pitié, F., Kokaram, A.: A ground truth bleed-through document image database. In: Proceedings of the Second International Conference on Theory and Practice of Digital Libraries. TPDL’12, pp. 185–196. Springer, Berlin (2012)
46. Sarkar, P., Nagy, G., Zhou, J., Lopresti, D.P.: Spatial sampling of printed patterns. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 344–351 (1998)
47. Shan, Q., Li, Z., Jia, J., Tang, C.K.: Fast image/video upsampling. ACM Trans. Graph. 27(5), 153:1–153:7 (2008)
48. Slimane, F., Kanoun, S., Hennebert, J., Alimi, A.M., Ingold, R.: A study on font-family and font-size recognition applied to arabic word images at ultra-low resolution. Pattern Recogn. Lett. 34, 209–218 (2013)
49. Sun, J., Xu, Z., Shum, H.Y.: Image super-resolution using gradient profile prior. In: CVPR 2008, 24–26 June 2008, Anchorage, Alaska, USA. IEEE Computer Society (2008)
50. Thouin, P.D., Chang, C.I.: A method for restoration of low-resolution document images. IJDAR 2(4), 200–210 (2000)
51. Turkan, M.: Nouvelles méthodes de synthèse de texture; application à la prédiction et à l’inpainting d’images, 1st edn. These, Université Rennes (2011)
52. Walha, R., Drira, F., Lebourgeois, F., Alimi, A.M.: Super-resolution of single text image by sparse representation. In: Proceeding of the Workshop on Document Analysis and Recognition. DAR ’12, pp. 22–29. ACM, New York, NY (2012)
53. Walha, R., Drira, F., Lebourgeois, F., Garcia, C., Alimi, A.M.: Multiple learned dictionaries based clustered sparse coding for the super-resolution of single text image. In: International Conference on Document Analysis and Recognition, ICDAR, pp. 484–488 (2013)
54. Walha, R., Drira, F., Lebourgeois, F., Garcia, C., Alimi, A.M.: Single textual image super-resolution using multiple learned dictionaries based sparse coding. In: International Conference on Image Analysis and Processing, ICIAP, vol. 2, pp. 439–448 (2013)
55. Walha, R., Drira, F., Lebourgeois, F., Garcia, C., Alimi, A.M.: Sparse coding with a coupled dictionary learning approach for textual image super-resolution. In: International Conference on Pattern Recognition, ICPR, pp. 4459–4464 (2014)
56. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
57. Weickert, J.: Coherence-enhancing diffusion of colour images. Image Vis. Comput. 17(3–4), 201–212 (1999)
58. van de Weijer, J., van Vliet, L.J., Verbeek, P.W., van Ginkel, M.: Curvature estimation in oriented patterns using curvilinear models applied to gradient vector fields. IEEE Trans. Pattern Anal. Mach. Intell. 23(9), 1035–1042 (2001)
59. Yan, Z., Lu, Y., Li, J.: Super resolution of text image by pruning outlier. In: Lu, B.L., Zhang, L., Kwok, J.T. (eds.) ICONIP (3). Lecture Notes in Computer Science, vol. 7064, pp. 649–656. Springer (2011)
60. Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. IEEE Trans. Image Process. 19(11), 2861–2873 (2010)
61. Yang, J., Wright, J., Ma, Y., Huang, T.: Image superresolution as sparse representation of raw image patches. In: CVPR (2008)
62. Yang, S., Wang, M., Chen, Y., Sun, Y.: Single-image super-resolution reconstruction via learned geometric dictionaries and clustered sparse coding. IEEE Trans. Image Process. 21(9), 4016–4028 (2012)
63. Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse-representations. In: Proceedings of the 7th International Conference on Curves and Surfaces, pp. 711–730. Springer, Berlin (2012)
64. Zhang, L., 0006, L.Z., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Trans. Image Process. 20(8), 2378–2386 (2011)
65. Zhang, L., Wu, X.: An edge-guided image interpolation algorithm via directional filtering and data fusion. IEEE Trans. Image Process. 15(8), 2226–2238 (2006)
66. Zhou, J., Lopresti, D., Sarkar, P., Nagy, G.: Spatial sampling effects on scanned 2-d patterns. Adv. Vis. Form Anal. 666–676 (1997)