Abstract Noise Reduction is one of the most important steps in very broad domain of image processing applications such as face identification, motion tracking, visual pattern recognition and etc. Texture images are covered a huge number of images where are collected as database in these applications. In this paper an approach is proposed for noise reduction in texture images which is based on real word spelling correction theory in natural language processing. The proposed approach is included two main steps. In the first step, most similar pixels to noisy desired pixel in terms of textural features are generated using local binary pattern. Next, best one of the candidates is selected based on two-gram algorithm. The quality of the proposed approach is compared with some of state of the art noise reduction filters in the result part. High accuracy, Low blurring effect, and low computational complexity are some advantages of the proposed approach.

References

[1] Allison L. and Dix T., A Bit-String Longest Common-Subsequence Algorithm, Information (13) (14) 255 255= 10 10PSNR LogMSE 11()MNi , j i , jijMSE X R / M N Impulse Noise Reduction for Texture Images Using Real Word Spelling ... 1029 Processing Letters, vol. 23, no. 5, pp. 305-310, 1986.

[2] Anjali P., Ajay S., and Spare S., A Review on Natural Image Denoising using Independent Component Analysis Technique, Advances in Computational Research, vol. 2, no. 1, pp. 6-14, 2010.

[3] Bassil Y. and Alwani M., Context-Sensitive Spelling Correction Using Google Web 1T 5- Gram Information, Computer and Information Science, vol. 5, no. 3, pp. 37-48, 2012.

[4] Brodatz P., Textures: A Photographic Album for Artists and Designers, Dover Press, 1966.

[5] Chen T., Ma K., and Chen L., Tri-State Median Filter for Image Denoising, IEEE Transactions on Image processing, vol. 8, no. 12, pp. 1834- 1838, 1999.

[6] Church K. and Gale W., A Comparison of the Enhanced Good-Turing and Deleted Estimation Methods for Estimating Probabilities of English Bigrams, Computer Speech and Language, vol. 5, no. 1, pp. 19-54, 1991.

[7] Damerau F., A Technique for Computer Detection and Correction of Spelling Errors, Communication of the ACM, vol. 7, no. 3, pp. 171-176, 1964.

[8] Fekri-Ershad S. and Tajeripour F., A Robust Approach for Surface Defect Detection Based on One Dimensional Local Binary Patterns, Indian Journal of Science and Technology, vol. 5, no. 8, pp. 3197-3203, 2012.

[9] Gupta K. and Gupta S., Image Denoising Techniques-A Review Paper, International Journal of Innovative Technology and Exploring Engineering, vol. 2, no. 4, pp. 6-9, 2013.

[10] Hamming R., Error Detecting and Error Correcting codes, The Bell System Technical Journal, vol. 29, no. 2, pp. 147-160, 1950.

[11] Islam A. and Inkpen D., Real Word Spelling Correction Using Google Web 1T 3-Grams, in Proceedings of Conference on Empirical Methods in Natural Language Processing, Singapore, pp. 1241-1249, 2009.

[12] Kuhn R. De M., A cache-based Natural Language Model for Speech Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 6, pp. 570-583, 1990.

[13] Kukich K., Techniques for Automatically Correcting Words in Text, ACM Computing Surveys, vol. 24, no. 4, pp. 377-439, 1992.

[14] Levenshtein V., Binary Codes Capable of Correcting Deletions, Insertions, and Reversals, Cybernetics and Control Theory, vol. 10, no. 8, pp. 707-710, 1966.

[15] Malini S. and Moni R., A Combined Spatial and Frequency Domain Approach for Removal of Impulse Noise from Images, International Journal of Engineering and Advanced Technology, vol. 4, no. 1, pp. 63-67, 2014.

[16] Nain A., Singhania S., Gupta S., and Bhushan B., A Comparative Study of Mixed Noise Removal Techniques, International Journal of Signal Processing, Image Processing and Pattern Recognition, vol. 7, no. 1, pp. 405-414, 2014.

[17] Niesler T. and Woodland P., A Variable-length Category-based N-gram Language Model, in Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings, Atlanta, pp. 164-167, 1996.

[18] Nisha S. and Mohideen K., A Novel Approach for Image Mixed Noise Reduction Using Wavelet Method, International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, no. 11, pp. 795-799,2013.

[19] Ojala T., Maeenpaeae T., and Pietikaeinen M., Texture Classification by Multi Predicate Local Binary Pattern Operators, in Proceedings of 15th International Conference on Pattern Recognition, Barcelona, pp. 951-954, 2000.

[20] Ojala T., Pietikainen M., and Maenpaa T., Multi Resolution Gray-scale and Rotation Invariant Texture Classification with Local Binary Patterns, IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971- 987, 2002.

[21] Petrov M. and Silva P., Image Processing Dealing with Texture, Willey and Sons Press, 2006.

[22] Pietik inen M., Ojala T., and Xu Z., Rotation- Invariant Texture Classification using Feature Distributions, Pattern Recognition, vol. 33, no. 1, pp. 43-52, 2000.

[23] Rahman M., Kumar M., and ShorifUddin M., Optimum Threshold Parameter Estimation of Wavelet Coefficients Using Fisher Discriminant Analysis for Speckle Noise Reduction, The International Arab Journal of Information Technology, vol. 11, no. 6, pp. 573-581, 2014.

[24] Riji R., Rajan J., Sijbers J., and Nair M., Iterative Bilateral Filter for Rician Noise Reduction in MR Images, Signal, Image and Video Processing, vol. 9, no. 7, pp. 1543-1548, 2014.

[25] Satpathy S., Panda S., Nagwanshi K., and Ardil C., Image Restoration in Non-Linear Filtering Domain using MDB Approach, International Journal of Information and Communication Engineering, vol. 6, no. 1, pp. 45-49, 2010.

[26] Srinivasan K. and Ebenezer D., A New Fast and Efficient Decision-Based Algorithm for Removal of High-Density Impulse Noises, IEEE Signal Processing Letters, vol. 14, no. 3, pp. 189-192, 2007. 1030 The International Arab Journal of Information Technology, Vol. 15, No. 6, November 2018

[27] Subg-Jea K. and Hoon Y., Center Weighted Median Filters and Their Applications to Image Enhancement, IEEE Transactions on Circuits and systems, vol. 38, no. 9, pp. 984-993, 1991.

[28] Tajeripour F. and Fekri-Ershad SH., Developing a Novel Approach for Stone Porosity Computing Using Modified Local Binary Patterns and Single Scale Retinex, Arabian Journal for Science and Engineering, vol. 39, no. 2, pp. 875-889, 2014.

[29] Wagner R. and Fischer M., The String-to-string Correction Problem, Journal of the Association for Computing Machinery, vol. 21, no. 1, pp. 168-173, 1974. Shervan Fekri-Ershad received his M.Sc. degree from Shiraz University, Iran in 2012, majored in Artificial Intelligence. He is currently a PhD student in the School of computer engineering, Shiraz University, Iran. He joined the department of computer engineering at Najafabad Branch, Islamic Azad University, Isfahan, Iran as assistant Professor in 2015. His research interests are visual inspection systems, texture analysis, surface defect detection, etc. Seyed Fakhrahmad received his BSc in Computer Engineering from Kharazmi University of Tehran (Iran) in 2003. His MSc and PhD degrees were received in Computer Engineering both from Department of Computer Science and Engineering, Shiraz University (Iran), in 2006 and 2011, respectively. He is currently an assistant professor in Department of Computer Science and Engineering, at Shiraz University. His research interests include Natural Language processing, Data Mining, Social Network Analysis and Fuzzy systems. Farshad Tajeripour received the B.Sc. and M.Sc. degrees in electrical engineering from Shiraz University, in 1994 and 1997, and the Ph.D. degree in electrical engineering from the Tarbiat Modarres University of Tehran, in 2007. In 2007, he joined the Department of Computer Engineering at Shiraz University, Shiraz, as an Assistant Professor. His research interests include digital image processing, machine vision, medical image processing, signal processing, and vision based inspection systems.