Adaptive Right Median Filter for Salt-and-Pepper Noise Removal

Yıl 2019, Cilt: 11 Sayı: 2, 542 - 550, 30.06.2019

Uğur Erkan Levent Gökrem Serdar Enginoğlu

https://doi.org/10.29137/umagd.495904

Cited By: 5

Öz

In image processing, nonlinear filters are commonly used as a pre-process for noise removal before applying any advanced processing such as classification and clustering to an image. The adaptive filters being a kind of the nonlinear filters mainly perform better than the others in salt-and-pepper noise. In this paper, we first define a new median method, i.e. right median
(rm). We then define a new adaptive nonlinear filter developed via rm, namely Adaptive Right Median Filter (ARMF), for saltand-pepper noise removal. Afterwards, we compare the results of ARMF with some of the known filters by using 12 test images and two image quality metrics: Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM). The results show that ARMF outperforms the other methods at all the noise density except 80% and 90% in the mean percentages. Finally, we discuss the need for further research.

Anahtar Kelimeler

Image denoising, Noise removal, Nonlinear filters

Kaynakça

• Asuni, N., & Giachetti, A. (2014). TESTIMAGES: a Large-scale Archive for Testing Visual Devices and Basic Image Processing Algorithms. In A. Giachetti (Ed.), Smart Tools and Apps for Graphics - Eurographics Italian Chapter Conference. The Eurographics Association. https://doi.org/10.2312/stag.20141242
• Bai, T., Tan, J., Hu, M., & Wang, Y. (2014). A novel algorithm for removal of salt and pepper noise using continued fractions interpolation. Signal Processing. https://doi.org/10.1016/j.sigpro.2014.03.023
• Chan, R. H., Ho, C. W., & Nikolova, M. (2005). Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization. IEEE Transactions on Image Processing. https://doi.org/10.1109/TIP.2005.852196
• Erkan, U., & Gökrem, L. (n.d.). Tuz-Biber Gürültüsünde Tekrarsız Medyan Filtre Median Filter without Repetition in Salt and Peppers Noise. GBAD) Gaziosmanpasa Journal of Scientific Research Araştırma Makalesi, ISSN, 2146–8168. Retrieved from http://dergipark.gov.tr/gbad
• Erkan, U., & Gökrem, L. (2018). A new method based on pixel density in salt and pepper noise removal. Turkish Journal of Electrical Engineering and Computer Sciences. https://doi.org/10.3906/elk-1705-256
• Erkan, U., Gökrem, L., & Enginoğlu, S. (2018). Different applied median filter in salt and pepper noise. Computers and Electrical Engineering, 70, 789–798. https://doi.org/10.1016/j.compeleceng.2018.01.019
• Erkan, U., & Kilicman, A. (2016). Two new methods for removing salt-and-pepper noise from digital images. ScienceAsia. https://doi.org/10.2306/scienceasia1513-1874.2016.42.028
• Esakkirajan, S., Veerakumar, T., Subramanyam, A. N., & PremChand, C. H. (2011). Removal of High Density Salt and Pepper Noise Through Modified Decision Based Unsymmetric Trimmed Median Filter. IEEE Signal Processing Letters. https://doi.org/10.1109/LSP.2011.2122333
• Gellert, A., & Brad, R. (2016). Context-based prediction filtering of impulse noise images. IET Image Processing, 10(6), 429–437. https://doi.org/10.1049/iet-ipr.2015.0702
• Han, J., Yue, J., Zhang, Y., & Bai, L. (2015). Local Sparse Structure Denoising for Low-Light-Level Image. IEEE Transactions on Image Processing. https://doi.org/10.1109/TIP.2015.2447735
• Hwang, H., & Haddad, R. A. (1995). Adaptive Median Filters: New Algorithms and Results. IEEE Transactions on Image Processing. https://doi.org/10.1109/83.370679
• Jiang, J., Zhang, L., & Yang, J. (2014). Mixed noise removal by weighted encoding with sparse nonlocal regularization. IEEE Transactions on Image Processing. https://doi.org/10.1109/TIP.2014.2317985
• Lee, G. S., Lee, S. H., Kim, G. O., Park, J. H., & Park, Y. S. (2016). A modified GrabCut using a clustering technique to reduce image noise. Symmetry. https://doi.org/10.3390/sym8070064
• Li, Z., Zheng, J., Zhu, Z., Yao, W., & Wu, S. (2015). Weighted guided image filtering. IEEE Transactions on Image Processing. https://doi.org/10.1109/TIP.2014.2371234
• Lin, C. H., Tsai, J. S., & Chiu, C. Te. (2010). Switching bilateral filter with a texture/noise detector for universal noise removal. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 19(9), 1434–1437. https://doi.org/10.1109/ICASSP.2010.5495475
• Liu, X., Jing, X. Y., Tang, G., Wu, F., & Ge, Q. (2017). Image denoising using weighted nuclear norm minimization with multiple strategies. Signal Processing. https://doi.org/10.1016/j.sigpro.2017.01.003
• Nguyen, M. P., & Chun, S. Y. (2017). Bounded self-weights estimation method for non-local means image denoising using minimax estimators. IEEE Transactions on Image Processing. https://doi.org/10.1109/TIP.2017.2658941
• Pattnaik, A., Agarwal, S., & Chand, S. (2012). A New and Efficient Method for Removal of High Density Salt and Pepper Noise Through Cascade Decision based Filtering Algorithm. Procedia Technology. https://doi.org/10.1016/j.protcy.2012.10.014
• Ponomarenko, N., Jin, L., Ieremeiev, O., Lukin, V., Egiazarian, K., Astola, J., … Jay Kuo, C. C. (2015). Image database TID2013: Peculiarities, results and perspectives. Signal Processing: Image Communication. https://doi.org/10.1016/j.image.2014.10.009
• Rafsanjani, H. K., Sedaaghi, M. H., & Saryazdi, S. (2017). An adaptive diffusion coefficient selection for image denoising. Digital Signal Processing: A Review Journal. https://doi.org/10.1016/j.dsp.2017.02.004
• Sun, C., Tang, C., Zhu, X., Li, X., & Wang, L. (2015). An efficient method for salt-and-pepper noise removal based on shearlet transform and noise detection. AEU - International Journal of Electronics and Communications, 69(12), 1823–1832. https://doi.org/10.1016/j.aeue.2015.09.007
• Toh, K. K. V., & Isa, N. A. M. (2010). Noise adaptive fuzzy switching median filter for salt-and-pepper noise reduction. IEEE Signal Processing Letters. https://doi.org/10.1109/LSP.2009.2038769
• Tomasi, C., & Manduchi, R. (1998). Bilateral filtering for gray and color images. Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271), 839–846. https://doi.org/10.1109/ICCV.1998.710815
• Tukey, J. W. (1977). Exploratory Data Analysis. Analysis. https://doi.org/10.1007/978-1-4419-7976-6
• Wang, Y., Wang, J., Song, X., & Han, L. (2016). An Efficient Adaptive Fuzzy Switching Weighted Mean Filter for Salt-And-Pepper Noise Removal. IEEE Signal Processing Letters, 23(11), 1582–1586. https://doi.org/10.1109/LSP.2016.2607785
• Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600–612. https://doi.org/10.1109/TIP.2003.819861
• Wang, Z., & Zhang, D. (1999). Progressive switching median filter for the removal of impulse noise from highly corrupted images. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing. https://doi.org/10.1109/82.749102
• William K. Pratt. (1975). Semiannual Technical Report. Image Processing Institute, University of Southern California.
• Xiong, R., Liu, H., Zhang, X., Zhang, J., Ma, S., Wu, F., & Gao, W. (2016). Image Denoising via Bandwise Adaptive Modeling and Regularization Exploiting Nonlocal Similarity. IEEE Transactions on Image Processing. https://doi.org/10.1109/TIP.2016.2614160
• Xu, J., Wang, L., & Shi, Z. (2014). A switching weighted vector median filter based on edge detection. Signal Processing. https://doi.org/10.1016/j.sigpro.2013.11.035
• Xu, S., Yang, X., & Jiang, S. (2017). A fast nonlocally centralized sparse representation algorithm for image denoising. Signal Processing. https://doi.org/10.1016/j.sigpro.2016.08.006