Abstract
An algorithm for filtering the images contaminated by additive white Gaussian noise is proposed. The algorithm uses the groups of Hadamard transformed patches of discrete cosine coefficients to reject noisy components according to Wiener filtering approach. The groups of patches are found by the proposed block similarity search algorithm of reduced complexity performed on block patches in transform domain. When the noise variance is small, the proposed filter uses an additional stage based on principal component analysis; otherwise the experimental Wiener filtering is performed. The obtained filtering results are compared to the state of the art filters in terms of peak signal-to-noise ratio and structure similarity index. It is shown that the proposed algorithm is competitive in terms of signal to noise ratio and almost in all cases is superior to the state of the art filters in terms of structure similarity.
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References
Pratt, W.K.: Digital Image Processing, 4th edn. Wiley-Interscience, New York (2007)
Buades, A., Coll, B., Morel, J.M.: A review of image denoising algorithms, with a new one. J. SIAM 2(4), 490–530 (2005)
Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3D transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080–2095 (2007)
Foi, A., Katkovnik, V., Egiazarian, K.: Pointwise shape-adaptive DCT for high-quality denoising and deblocking of grayscale and color images. IEEE Trans. Image Process. 16(5), 1395–1411 (2007)
Chatterjee, P., Milanfar, P.: Is denoising dead? IEEE Trans. Image Process. 19(4), 895–911 (2010)
Aharon, M., Elad, M., Bruckstein, A.M.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54(11), 4311–4322 (2006)
Dong, W., Zhang, L., Shi, G., Li, X.: Nonlocally centralized sparse representation for image restoration. IEEE Trans. Image Process. 22(4), 1620–1630 (2013)
He, N., Wang, J.-B., Zhang, L.-L., Xu, G.-M., Lu, K.: Non-local sparse regularization model with application to image denoising. Multimed. Tools Appl. 75(5), 2579–2594 (2016)
Pogrebnyak, O., Lukin, V.V.: Wiener discrete cosine transform-based image filtering. J. Electron. Imaging 21(4), 043020-1–043020-1 (2012). doi:10.1117/1.JEI.21.4.043020. USA, ISSN 1017-9909
Fevralev, D., Lukin, V., Ponomarenko, N., Abramov, S., Egiazarian, K., Astola, J.: Efficiency analysis of color image filtering. EURASIP J. Adv. Signal Process. 2011, 41 (2011). doi:10.1186/1687-6180-2011-41
Lebrun, M.: An analysis and implementation of the BM3D image denoising method http://dx.doi.org/. Image Process. Line 2, 175–213 (2012). http://dx.doi.org/10.5201/ipol.2012.l-bm3d
Lukin, V., Abramov, S., Krivenko, S., Kurekin, A., Pogrebnyak, O.: Analysis of classification accuracy for pre-filtered multichannel remote sensing data. Expert Syst. Appl. 40(16), 6400–6411 (2013). doi:10.1016/j.eswa.2013.05.061. ISSN 0957-4174
Egiazarian, K., Astola, J., Ponomarenko, N., Lukin, V., Battisti, F., Carli, M.: New full-reference quality metrics based on HVS. In: CD-ROM Proceedings of the Second International Workshop on Video Processing and Quality Metrics, Scottsdale, USA (2006), 4 p
Ponomarenko, N., Silvestri, F., Egiazarian, K., Carli, M., Lukin, V.: On between-coefficient contrast masking of DCT basis functions. In: CD-ROM Proceedings of Third International Workshop on Video Processing and Quality Metrics for Consumer Electronics VPQM-07 (2007), 4 p
Zhang, L., Zhang, L., Mou, X., Zhang, D.: FSIM: a feature similarity index for image quality assessment. IEEE Trans. Image Process. 20(8), 2378–2386 (2011)
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). doi:10.1109/TIP.2003.819861. ISSN 1057-7149
Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multiscale structural similarity for image quality assessment. In: Conference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, 1 November 2003, vol. 2, pp. 1398–1402 (2004). doi:10.1109/ACSSC.2003.1292216
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This work partially was supported by Instituto Politecnico Nacional as a part of research project SIP#20161173.
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Callejas Ramos, A.I., Felipe-Riveron, E.M., Manrique Ramirez, P., Pogrebnyak, O. (2017). Image Filter Based on Block Matching, Discrete Cosine Transform and Principal Component Analysis. In: Sidorov, G., Herrera-Alcántara, O. (eds) Advances in Computational Intelligence. MICAI 2016. Lecture Notes in Computer Science(), vol 10061. Springer, Cham. https://doi.org/10.1007/978-3-319-62434-1_34
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DOI: https://doi.org/10.1007/978-3-319-62434-1_34
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