Abstract
The resolution enhancement of textual images poses a significant challenge mainly in the presence of noise. The inherent difficulties are twofold. First is the reconstruction of an upscaled version of the input low-resolution image without amplifying the effect of noise. Second is the achievement of an improved visual image quality and a better OCR accuracy. Classically, the issue is addressed by the application of a denoising step used as a preprocessing or a post-processing to the magnification process. Starting by a denoising process could be more promising to avoid any magnified artifacts while proceeding otherwise. However, the state of the art underlines the limitations of denoising approaches faced with the low spatial resolution of textual images. Recently, sparse coding has attracted increasing interest due to its effectiveness in different reconstruction tasks. This study proves that the application of an efficient sparse coding-based denoising process followed by the magnification process can achieve good restoration results even if the input image is highly noisy. The main specificities of the proposed sparse coding-based framework are: (1) cascading denoising and magnification of each image patch, (2) the use of sparsity stemmed from the non-local self-similarity given in textual images and (3) the use of dual dictionary learning involving both online and offline dictionaries that are selected adaptively for each local region of the input degraded image to recover its corresponding noise-free high-resolution version. Extensive experiments on synthetic and real low-resolution noisy textual images are carried out to validate visually and quantitatively the effectiveness of the proposed system. Promising results, in terms of image visual quality as well as character recognition rates, are achieved when compared it with the state-of-the-art approaches.
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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). https://doi.org/10.1109/tsp.2006.881199
Alaei, A., Conte, D., Blumenstein, M., Raveaux, R.: Document image quality assessment based on texture similarity index. In: Proceedings of 12th IAPR Workshop on Document Analysis Systems (DAS), Santorini, Greece, pp. 132–137 (2016). https://doi.org/10.1109/DAS.2016.33
Alaei, A., Conte, D., Raveaux, R.: Document image quality assessment based on improved gradient magnitude similarity deviation. In: Proceedings of 13th International Conference on Document Analysis and Recognition (ICDAR), Nancy, France, pp. 176–180 (2015). https://doi.org/10.1109/ICDAR.2015.7333747
Banerjee, J., Jawahar, C.V.: Super-resolution of text images using edge-directed tangent field. In: Proceedings of DAS, pp. 76–83. Washington, DC, USA (2008). https://doi.org/10.1109/DAS.2008.26
Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: Proceedings of CVPR, pp. 60–65 (2005). https://doi.org/10.1109/CVPR.2005.38
Caner, G., Haritaoglu, I.: Shape-dna: effective character restoration and enhancement for Arabic text documents. In: Proceedings of the International Conference on Pattern Recognition (ICPR), pp. 2053–2056. Washington, DC, USA (2010). https://doi.org/10.1109/ICPR.2010.506
Chatterjee, P., Milanfar, P.: Clustering-based denoising with locally learned dictionaries. IEEE Trans. Image Process. 18(7), 1438–1451 (2009). https://doi.org/10.1109/TIP.2009.2018575
Chen, G., Zhu, F., Ann Heng, P.: An efficient statistical method for image noise level estimation. In: The IEEE International Conference on Computer Vision (ICCV) (2015)
Chen, S.S., Donoho, D.L., Saunders, M.A.: Atomic decomposition by basis pursuit. SIAM Rev. 43(1), 129–159 (2001). https://doi.org/10.1137/S003614450037906X
Chou, S.L., Yu, S.S.: Sorting qualities of handwritten Chinese characters for setting up a research database. In: International Conference on Document Analysis and Recognition, pp. 474–477 (1993)
Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.O.: Image denoising by sparse 3-d transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080–2095 (2007). https://doi.org/10.1109/TIP.2007.901238
Dai, S., Han, M., Wu, Y., Gong, Y.: Bilateral back-projection for single image super resolution. In: Proceedings of ICME, pp. 1039–1042 (2007). https://doi.org/10.1109/ICME.2007.4284831
Dalley, G., Freeman, W.T., Marks, J.: Single-frame text super-resolution: a Bayesian approach. In: Proceedings of ICIP, pp. 3295–3298 (2004)
Davis, G., Mallat, S., Avellaneda, M.: Adaptive greedy approximations. Constr. Approx. 13(1), 57–98 (1997). https://doi.org/10.1007/BF02678430
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)
Dong, W., Zhang, L., Shi, G., Wu, X.: Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization. IEEE Trans. Image Process. 20(7), 1838–1857 (2011). https://doi.org/10.1109/TIP.2011.2108306
Drira, F., Lebourgeois, F.: Denoising textual images using local/non-local smoothing filters: a comparative study. In: Proceedings of ICFHR, pp. 521–526 (2012). https://doi.org/10.1109/ICFHR.2012.198
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: Proceedings of ICDAR, pp. 321–325 (2009)
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)
Elad, M., Aharon, M.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process. 15(12), 3736–3745 (2006). https://doi.org/10.1109/TIP.2006.881969
Fadili, M.J., Starck, J.L., Murtagh, F.: Inpainting and zooming using sparse representations. Comput. J. 52(1), 64–79 (2007)
Freeman, G., Fattal, R.: Image and video upscaling from local self-examples. ACM Trans. Graph. 28(3), 1–10 (2010)
Hale, C., Smith, E.H.B.: Human image preference and document degradation models. In: Proceedings of ICDAR, Curitiba, Brazil, pp. 257–261 (2007). https://doi.org/10.1109/ICDAR.2007.135
Hartley, R.T., Crumpton, K.: Quality of OCR for degraded text images. In: ACM Conference on Digital Library, pp. 228–229 (1999)
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)
Kulesh, V., Schaffer, K., Sethi, I.K., Schwartz, M.: Handwriting quality evaluation. In: Proceedings of Second International Conference on Advances in Pattern Recognition (ICAPR), Brazil, pp. 157–165 (2001)
Kumar, D., Ramakrishnan, A.G.: Quad: quality assessment of documents. In: International Workshop on Camera-based Document Analysis and Recognition, pp. 79–84 (2011)
Kumar, J., Chen, F., Doermann, D.S.: Sharpness estimation for document and scene images. In: Proceedings of ICPR, Tsukuba, Japan, pp. 3292–3295 (2012)
Kumar, V., Bansal, A., Tulsiyan, G.H., Mishra, A., Namboodiri, A.M., Jawahar, C.V.: Sparse document image coding for restoration. In: Proceedings of ICDAR, pp. 713–717 (2013)
Lee, H., Battle, A., Raina, R., Ng, A.Y.: Efficient sparse coding algorithms. In: Proceedings of Neural Information Processing Systems (NIPS), pp. 801–808 (2007)
Lelore, T., Bouchara, F.: FAIR: a fast algorithm for document image restoration. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 2039–2048 (2013). https://doi.org/10.1109/TPAMI.2013.63
Li, R., Zhang, Y.J.: A hybrid filter for the cancellation of mixed Gaussian noise and impulse noise. In: IEEE International Conference on Information Communications and Signal Processing, pp. 508–512 (2003)
Li, X., Orchard, M.T.: New edge-directed interpolation. IEEE Trans. Image Process. 10(10), 1521–1527 (2001). https://doi.org/10.1109/83.951537
Liu, X., Tanaka, M., Okutomi, M.: Single-image noise level estimation for blind denoising. IEEE Trans. Image Process. 22(12), 5226–5237 (2013). https://doi.org/10.1109/TIP.2013.2283400
Lopresti, D.P., Zhou, J., Nagy, G., Sarkar, P.: Spatial sampling effects in optical character recognition. In: Proceedings of ICDAR, pp. 309–314 (1995)
Lu, H., Kot, A.C., Shi, Y.Q.: Distance-reciprocal distortion measure for binary document images. IEEE Signal Process. Lett. 11(2), 228–231 (2004). https://doi.org/10.1109/LSP.2003.821748
Luong, H., Philips, W.: Robust reconstruction of low-resolution document images by exploiting repetitive character behaviour. IJDAR 11(1), 39–51 (2008). https://doi.org/10.1007/s10032-008-0068-2
Mairal, J., Elad, M., Sapiro, G.: Sparse representation for color image restoration. In: IEEE Transactions on Image Processing, pp. 53–69. ITIP (2007)
Mirkin, B.: Clustering for Data Mining: A Data Recovery Approach, 1st edn. Chapman and Hall/CRC, Boca Raton (2005)
Namane, A., Sid-Ahmed, M.A.: Character scaling by contour method. IEEE Trans. Pattern Anal. Mach. Intell. 12(6), 600–606 (1990)
Nayef, N., Chazalon, J., Gomez-Kramer, P., Ogier, J.M.: Efficient example-based super-resolution of single text images based on selective patch processing. In: Proceedings of DAS, pp. 227–231 (2014). https://doi.org/10.1109/DAS.2014.25
Obafemi-Ajayi, T., Agam, G.: Character-based automated human perception quality assessment in document images. IEEE Trans. Syst. Man Cybern. Part A 42(3), 584–595 (2012). https://doi.org/10.1109/TSMCA.2011.2170417
Obafemi-Ajayi, T., Agam, G., Frieder, O.: Evaluation of human perception of degradation in document images. In: Proceedings of Document Recognition and Retrieval XVII, part of the IS&T-SPIE Electronic Imaging Symposium, San Jose, CA, USA, p. 75340T (2010). https://doi.org/10.1117/12.838748
Pan, J., Hu, Z., Su, Z., Yang, M.: Deblurring text images via L0-regularized intensity and gradient prior. In: Proceedings of CVPR, Columbus, OH, USA, pp. 2901–2908 (2014). https://doi.org/10.1109/CVPR.2014.371
Pan, J., Hu, Z., Su, Z., Yang, M.: L\(_{\text{0 }}\)-regularized intensity and gradient prior for deblurring text images and beyond. IEEE Trans. Pattern Anal. Mach. Intell. 39(2), 342–355 (2017). https://doi.org/10.1109/TPAMI.2016.2551244
Park, J., Kang, H., Lee, S.: Automatic quality measurement of gray-scale handwriting based on extended average entropy. In: Proceedings of ICPR, Barcelona, Spain, pp. 4426–4429 (2000). https://doi.org/10.1109/ICPR.2000.902949
Pati, Y.C., Rezaiifar, R., Rezaiifar, Y.C.P.R., Krishnaprasad, P.S.: Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition. In: Proceedings of the 27th Annual Asilomar Conference on Signals, Systems, and Computers, pp. 40–44 (1993)
Peng, X., Cao, H., Subramanian, K., Prasad, R., Natarajan, P.: Automated image quality assessment for camera-captured OCR. In: Proceedings of ICIP, Brussels, Belgium, pp. 2621–2624 (2011). https://doi.org/10.1109/ICIP.2011.6116204
Pham, T.A., Delalandre, M.: Effective decompression of JPEG document images. IEEE Trans. Image Process. 25(8), 3655–3670 (2016). https://doi.org/10.1109/TIP.2016.2576024
Portilla, J.: Full blind denoising through noise covariance estimation using Gaussian scale mixtures in the wavelet domain. In: International Conference on Image Processing, vol. 2, pp. 1217–1220 (2004). https://doi.org/10.1109/ICIP.2004.1419524
Pratt, W.K.: Digital Image Processing, 3rd edn. Wiley, New York (1978)
Pratt, W.K.: Digital Image Processing: PIKS Inside, 3rd edn. Wiley, New York (2001)
Rice, S.V.: Measuring the accuracy of page-reading systems. Ph.D. thesis, UNLV, Las Vegas (1996)
Rubinstein, R., Bruckstein, A., Elad, M.: Dictionaries for sparse representation modeling. Proc. IEEE 98(6), 1045–1057 (2010). https://doi.org/10.1109/JPROC.2010.2040551
Rusiñol, M., Chazalon, J., Ogier, J.: Combining focus measure operators to predict OCR accuracy in mobile-captured document images. In: Proceedings of DAS, Tours, France, pp. 181–185 (2014). https://doi.org/10.1109/DAS.2014.11
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)
Smith, E.H.B., Andersen, T.L.: Text degradations and OCR training. In: Proceedings of ICDAR, Seoul, Korea, pp. 834–838 (2005). https://doi.org/10.1109/ICDAR.2005.226
Thouin, P., Du, Y., Chang, C.I.: Low resolution expansion of gray scale text images using Gibbs–Markov random field model. In: Proceedings of Symposium on Document Image Understanding Technologies, Columbia, MD, pp. 41–47 (2001)
Thouin, P.D., Chang, C.I.: A method for restoration of low-resolution document images. IJDAR 2(4), 200–210 (2000)
Turkan, M.: Nouvelles méthodes de synthèse de texture; application à la prédiction et à l’inpainting d’images. Thesis, Université Rennes 1 (2011)
Walha, R., Drira, F., Lebourgeois, F., Alimi, A.M.: Super-resolution of single text image by sparse representation. In: Proceedings of the workshop on Document Analysis and Recognition (DAR), pp. 22–29. ACM, New York, NY, USA (2012). https://doi.org/10.1145/2432553.2432558
Walha, R., Drira, F., Lebourgeois, F., Alimi, A.M., Garcia, C.: Resolution enhancement of textual images: a survey of single image-based methods. IET Image Process. 10(4), 325–337 (2016). https://doi.org/10.1049/iet-ipr.2015.0334
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: Proceedings of International Conference on Document Analysis and Recognition, ICDAR, pp. 484–488 (2013)
Walha, R., Drira, F., Lebourgeois, F., Garcia, C., Alimi, A.M.: Single textual image super-resolution using multiple learned dictionaries based sparse coding. In: Proceedings of International Conference on Image Analysis and Processing, ICIAP, Part 2, pp. 439–448 (2013)
Walha, R., Drira, F., Lebourgeois, F., Garcia, C., Alimi, A.M.: Histogram of structure tensors: application to pattern clustering. In: Proceedings of WSCG, Plzen, pp. 345–352 (2014)
Walha, R., Drira, F., Lebourgeois, F., Garcia, C., Alimi, A.M.: A sparse coding based approach for the resolution enhancement and restoration of printed and handwritten textual images. In: Proceedings of ICFHR, Greece, pp. 696–701 (2014)
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: Proceedings of ICPR, Stockholm, pp. 4459–4464 (2014)
Walha, R., Drira, F., Lebourgeois, F., Garcia, C., Alimi, A.M.: Resolution enhancement of textual images via multiple coupled dictionaries and adaptive sparse representation selection. Int. J. Doc. Anal. Recognit. 18(1), 87–107 (2015). https://doi.org/10.1007/s10032-014-0235-6
Walha, R., Drira, F., Lebourgeois, F., Garcia, C., Alimi, A.M.: Joint denoising and magnification of noisy low-resolution textual images. In: Proceedings of ICDAR, Nancy, France, pp. 871–875 (2015)
Weickert, J.: A review of nonlinear diffusion filtering. In: Proceedings of International Conference on Scale-Space Theory in Computer Vision, pp. 3–28 (1997)
Xu, J., Ye, P., Li, Q., Liu, Y., Doermann, D.S.: No-reference document image quality assessment based on high order image statistics. In: Proceedings of ICIP, Phoenix, AZ, USA, pp. 3289–3293 (2016). https://doi.org/10.1109/ICIP.2016.7532968
Yan, Z., Lu, Y., Li, J.: Super resolution of text image by pruning outlier. Proc. ICONIP 7064, 649–656 (2011)
Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. IEEE Trans. Image Process. 19(11), 2861–2873 (2010). https://doi.org/10.1109/TIP.2010.2050625
Yang, M.C., Wang, Y.C.F.: A self-learning approach to single image super-resolution. IEEE Trans. Multimed. 15(3), 498–508 (2013)
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)
Ye, P., Doermann, D.S.: Document image quality assessment: a brief survey. In: Proceedings of ICDAR, Washington, DC, USA, pp. 723–727 (2013). https://doi.org/10.1109/ICDAR.2013.148
Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse-representations. In: Proceedings of International conference on Curves and Surfaces, pp. 711–730 (2012). https://doi.org/10.1007/978-3-642-27413-847
Zhang, M., Desrosiers, C., Zhang, C., Cheriet, M.: Effective document image deblurring via gradient histogram preservation. In: Proceedings of ICIP, Quebec City, QC, Canada, pp. 779–783 (2015). https://doi.org/10.1109/ICIP.2015.7350905
Zheng, Y., Kang, X., Li, S., He, Y., Sun, J.: Real-time document image super-resolution by fast matting. In: Proceedings of DAS, pp. 232–236 (2014). https://doi.org/10.1109/DAS.2014.32
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Walha, R., Drira, F., Lebourgeois, F. et al. Handling noise in textual image resolution enhancement using online and offline learned dictionaries. IJDAR 21, 137–157 (2018). https://doi.org/10.1007/s10032-017-0294-6
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DOI: https://doi.org/10.1007/s10032-017-0294-6