Implementation of Smoothing and Noise Reduction for Digital Image Quality Enhancement Using Neighborhood Operations
DOI:
https://doi.org/10.35194/mji.v17i2.5893Keywords:
Smoothing, Noise, Reduction, Neighborhood, Digital imageAbstract
The rapid growth in digital imaging technology has brought profound changes to various sectors. However, the quality of digital images is often compromised by noise, such as gaussian noise, salt-and-pepper noise, and spackle noise. Noise not only reduces the aesthetics of an image, but can also hinder image analysis and interpretation. In addition, it can obscure important details in the image and reduce the clarity and accuracy of visual analysis. To improve the quality of digital images, effective smoothing and noise removal techniques are needed, one of which is the neighborhood operation. The main purpose of smoothing and noise removal is to improve visual quality so that images are clearer and easier to analyze. In this study, a mean filter is used for smoothing and a median filter is used for noise reduction. Combine mean and median filtering in this study is directly aligned with its emphasis on a pixel-domain, low-level analysis of convolution-based smoothing. The mask used has a size of 5, 9, 25, or 49 points as the kernel in the convolution mask operation to remove noise and smooth the image at the same time. The digital image processing application was created following the waterfall software development model stages.The BMP format was selected in this study primarily to ensure data integrity and experimental control. To measure the quality of the image produced after the smoothing process, the Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR) standards are used. From the experimental results, the MSE values for mask sizes 5, 9, 25, and 49 are 12.96, 14.36, 14.72, and 16.80, respectively. Meanwhile, the PSNR values were 37, 36.56, 36.54, and 35.87, respectively. From the image quality results in the form of MSE and PSNR, it can be seen that the larger the mask size, the greater the MSE value, but the smaller the PSNR value. The smaller the PSNR value, the worse the image quality, and vice versa. This is supported by visual analysis, where more details of the original image are lost. However, the PSNR value is still in the range of 30-40 dB, which means the quality is still in the Good category. The quality is still acceptable with minimal distortion. The quality of the results is still very close to the original image. The highest image quality is found in mask 5.References
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[4] M. Ihsan, L. Sofinah Harahap, and F. Hady Raya, “Analysis of Pattern Recognition Methods in Digital Image Processing: A Review of Recent Literature,” Jurnal Program Mahasiswa Kreatif, vol. 9, no. 1, pp. 171–175, 2025, doi: 10.32832/pkm.
[5] A. E. Ilesanmi and T. O. Ilesanmi, “Methods for image denoising using convolutional neural network: a review,” Complex and Intelligent Systems, vol. 7, no. 5, pp. 2179–2198, Oct. 2021, doi: 10.1007/s40747-021-00428-4.
[6] A. Fauzi, “Pengurangan Derau (Noise) Pada Citra Paper Dokumen menggunakan Metode Gaussian Filter dan Median Filter,” KAKIFIKOM (Kumpulan Artikel Karya Ilmiah Fakultas Ilmu Komputer, vol. 04, no. 01, pp. 7–15, 2022.
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[11] C. Solomon and T. Breckon, Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab. Wiley-Blackwell, 2010.
[12] A. C. Bovik, Handbook of Image and Video Processing (Communications, Networking and Multimedia), 2nd ed. Academic Press Inc, 2005.
[13] H. Pangaribuan and J. E. Candra, “Optimalisasi Kualitas Citra Digital Dengan Metode Ketetanggaan Piksel,” Jurnal Ilmiah Informatika, vol. 7, no. 01, 2019.
[14] R. Oktafian, “Image Smoothing Menggunakan Metode Mean Filtering,” Journal of Information Technology and Computer Science (JOINTECS), vol. 4, no. 2, pp. 57–62, 2019.
[15] I. G. A. Gunadi, I. G. A. Wicaksana, M. R. Dwija, I. P. A. S. Putra, and P. P. Putra, “Pengurangan Noise Pada Citra Digital Menggunakan Filter Aritmatik Mean, Harmonik Mean, Gaussian, Max, Min, Dan Median Dengan Membandingkan Psnr,” Jurnal Ilmu Komputer Indonesia(JIK), vol. 5, no. 2, pp. 34–44, 2020.
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[17] A. Wedianto, H. L. Sari, and Y. S. H, “Analisa Perbandingan Metode Filter Gaussian, Mean Dan Median Terhadap Reduksi Noise,” Jurnal Media Infotama, vol. 12, no. 1, pp. 21–30, 2016.
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[19] I. Sommerville, Software Engineering , 10th ed. Pearson Education Limited, 2016.
[20] R. Szeliski, Computer Vision: Algorithms and Applications, 2nd ed. Springer, 2022.
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2025-12-31
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