Digital Filter Application for Image Containing Salt and Pepper Noise Using the Directional Weighted Minimum Deviation Method
DOI:
https://doi.org/10.26874/jt.vol20no2.418Keywords:
Digital Filter, DWMD, Image, Salt and Pepper noiseAbstract
Telecommunications technology growing rapidly, from the initial communication using letters, and now has reached the stage of image and video communication. In the process of transmitting data, both sound and images cannot be separated from noise. One solution to answer these problems is develop Digital Filter technology. In this research, a digital filter realized with an image object using the DWMD Filter method by detecting the type of salt and pepper noise. The DWMD Filter is a direction and standard deviation based digital data processing that improves the Median filter. With the PSNR, it is known that DWMD can produce better images than the Median Filter. This DWMD Filter method is coupled with an automatic Threshold determination to help the filter process to be faster where the largest PSNR difference is taken. The experimental results show that at a noise level of 5% - 65% the DWMD method produces a PSNR of 21-36 dB compared to the Median Filter of 13-29 dB. This research has an output in the form a desktop application that is equipped with features that can add salt and pepper noise in various densities and access images that can be converted into grayscale format.
References
Al-Azzeh, J., Zahran, B., & Alqadi, Z. (2018). Salt and Pepper Noise: Effects and Removal. International Journal on Electrical Engineering and Informatics.
Bharati, S., Khan, T. Z., & Podder, P. (2020). A comparative analysis of image denoising problem:noise models, denoising filters and applications. preprints.org.
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, 1479-1485.
Duth, P. S., & Deepa, M. M. (2018). Color detection in RGB-modeled images using MAT LAB. International Journal of Engineering & Technology, 29-33.
Fu, B., Zhao, X.-Y., Ren, Y.-G., Li, X.-M., & Wang, X.-H. (2019). A salt and pepper noise image denoising method based on the generative classification. Multimedia Tools and Applications, 12043–12053. doi:https://doi.org/10.1007/s11042-018-6732-8
Gunadi, I. G., Wicaksana, I. G., Dwija, M. R., Putra, I. P., & Putra, P. P. (2020). Pengurangan Noise Pada Citra Digital Menggunakan Filter Aritmatik Mean, Harmonik Mean, Gaussian, Max, Min, Dan Median Dengan Membandingkan PSNR. Jurnal Ilmu Komputer Indonesia(JIK), 35-44.
Harmayani, & Rahim, R. (2017). 24 Bit Image Noise Reduction with Median Filtering Algorithm. International Journal of Recent Trends in Engineering & Research (IJRTER), 1-5.
Jassim, F. A. (2013). Kriging Interpolation Filter to Reduce High Density Salt and Pepper Noise. World of Computer Science and Information Technology Journal (WCSIT) , 8-14.
Kurniawan, B. S., Sentinuwo, S. R., & Lantang, O. A. (2016). Aplikasi Pengenal Citra Nomor Kendaraan Bermotor Mengunakan Metode Template Matching. E-journal Teknik Informatika, 7-12.
Liang, H., Li, N., & Zhao, S. (2021). Salt and Pepper Noise Removal Method Based on a Detail-Aware Filter. The 3rd International Conference on Symmetry. sciforum.
Liu, H. (2020). Grayscale and gray image. In Robot Systems for Rail Transit Applications (pp. 123-124). Matthew Deans.
Mondal, P. J., & Mukhopadhyay, S. (2010). A Novel Directional Weighted Minimum Deviation (DWMD) Based Filter for Removal of Random Valued Impulse Noise. Proceedings of ICCS (pp. 214-220). Burdwan, West Bengal: Department of Computer Science, The University of Burdwan.
Novian, A. (2019). 42Perancangan Aplikasi Denoise Citra Dengan Menerapkan New Daptive Based Median Filter. Building of Informatics, Technology and Science (BITS), 42-47.
Pinki, & Mehra, R. (2016). Estimation of the Image Quality under Different Distortions. International Journal Of Engineering And Computer Science, 17291-17296.
Tasni, K. (2018). Penerapan Algoritma Band Reject Filter untuk Mereduksi Noise pada Citra Digital. Jurnal Pelita Informatika, 341-346.
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