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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM66360

Advanced Filtering Strategies for Image Denoising

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM66360

Volume:

10 |

7 |

Jul 2026

Pages:

1-7

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM66360

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

AB

Arfa Bhandari

Computer Science

Karnataka state Akkamahadevi Womens University

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Mentors

DM

Dr.Aziz Makandar

Computer Science

Karnataka state Akkamahadevi Womens University

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Abstract

These days, visual statistics is an increasing number of despatch-ed within the shape of digital pictures, making the identity of noisy statistics is a common task in lots of research and alertness domain names. Many noise discount strategies had been advanced in the contemporary era to get rid of noise while keeping image facts. The assignment of eliminating noise from an unique picture remains a hard one for researchers. The number one cognizance of this examine is on picture denoising and filtering. For performing a comparative exam of current denoising strategies, along with linear and nonlinear filters. salt, pepper noise, Gaussian noise, and speckle noise are examples of various noise fashions. PSNR of shade photos gives a amount degree of contrast.

KEYWORDS

picture denoising linear and nonlinear filters Gaussian noise salt and pepper noise speckle noise.

Cite This Article

Arfa Bhandari, Dr.Aziz Makandar, "Advanced Filtering Strategies for Image Denoising," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 7, pp. 1-7, Jul 2026. https://doi.org/10.55041/IJSREM66360

Full Article/Conclusion

In this paper, two forms of images that have been restored successfully via simple filtering techniques to recover the images that are contaminated by noise. The entire process of denoised image has been incorporated in ANACONDA-3. Besides denoising of the images, comparison of filtering techniques has also been done. The signal to noise ratios of the denoised images through these techniques have been compared in order to compare the effectiveness of these techniques. As it is seen, median filter outperforms the mean filter in impulsive and Gaussian noise and vice versa.

References

[1]  Fan, L., Zhang, F., Fan, H., & Zhang, C. (2019). Brief review of image denoising techniques. Visual Computing for Industry, Biomedicine, and Art, 2(1). https://doi.org/10.1186/s42492-019-0016-7. [2]   Várkonyi-Kóczy, A. R. (2010). New advances in digital image processing. Memetic Computing, 2(4), 283–304. https://doi.org/10.1007/s12293-010-0046-3. [3]  Singhal, A., & Mourya, D. (2014). Image Denoising Using Wavelets. 3rd International Conference on System Modeling & Advancement in Research Trends (SMART), 171-175. https://doi.org/10.25007/ajnu.v9n1a587. [4]   Alizoti, A. (2021). Computer – Aided RGB analyses of images. Technium Romanian Journal of Applied Sciences and Technology, 3(8), 1–12. https://doi.org/10.47577/technium.v3i8.4691. [5]  Rajni, R., & Anutam, A. (2014). Image Denoising Techniques - An Overview. International Journal of Computer Applications, 86(16), 13–17. https://doi.org/10.5120/15069-3436. [6]  Singh, S. (2017). A Relative Study of Various Noise Removal Techniques in Images. Journal of Web Engineering & Technology, 4(2), 22-30. [7]   Diwakar, M., & Kumar, M. (2018). A review on CT image noise and its denoising. Biomedical Signal Processing and Control, 42, 73–88. https://doi.org/10.1016/j.bspc.2018.01.010. [8]  Charde, P. (2013). A Review on Image Denoising Using Wavelet Transform and Median Filter over. International Journal of Technology Enhancements and Emerging Engineering Research, 1(4). [9]  Jaiswal, A., Upadhyay, J., & Somkuwar, A. (2014). Image denoising and quality measurements by using filtering and wavelet based techniques. AEU - International Journal of Electronics and Communications, 68(8), 699–705. https://doi.org/10.1016/j.aeue.2014.02.003. Kang, C. C., & Wang, W. J. (2009). Fuzzy reasoning-based directional median filter design. Signal Processing, 89(3), 344–351. https://doi.org/10.1016/j.sigpro.2008.09.003.

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Publication Date

7/27/2026

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Double-Blind Peer Review

Review Rounds: 2

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