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Soft Weighted Median Filter Method for Improved Image Segmentation with Noise

JUTI: Jurnal Ilmiah Teknologi Informasi · Vol. 16 No. 2 (2018) · pp. 125–133

DOI
10.12962/j24068535.v16i2.a721
ISSN
2406-8535, 1412-6389
Published
2018-10-09
Publisher
Institut Teknologi Sepuluh Nopember
Language
eng

Abstract

Soft Weighted Median Filter Method (SWMF) is one of the new methods for noise filtering in image processing. This method is used for two types of noise in images, there is fixed valued noise (FVN) and random valued noise (RVN). Fixed valued noise is a noise type with an unchanged value, it changes the pixel value of the image to the maximum and minimum values (0 and 255), while random valued noise is a noise type with a changed value. An example of fixed valued noise is salt & pepper noise, while for random valued noise can be exemplified as gaussian, poisson, speckle, and localvar noise.Based on previous research, SWMF method can be applied to all images with all kinds of noise (FVN and RVN) and able to reduce the noise well. This method has a higher PSNR value than other methods, especially for random valued noise types such as: gaussian, speckle, and localvar noise.In this study, we propose to examine the performance of the SWMF method further by comparing this method with other methods such as Median Filter, Mean Filter, Gaussian Filter, and Wiener Filter in an image segmentation process. The image segmentation process in this research is based on area detection using Top-Hat transform and Otsu thresholding and line detection using Sobel edge detection. The performance measurement process uses the calculation of sensitivity value, specificity, and accuracy on the image segmentation with the groundtruh image.The results show that Soft Weighted Median Filter method can improve the quality of image segmentation with the average accuracy of 95.70% by reducing fixed value noise and random valued noise in the images.

Collaboration network of the authors

23 authors · 39 co-authorship links · 2 across different organisations

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  • Institut Teknologi Sepuluh Nopember
  • UIN Sunan Ampel Surabaya
  • Politeknik Negeri Nusa Utara
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Chastine FatichahInstitut Teknologi Sepuluh Nopember4
Yasinta Oktaviana Legu RemaAffiliation unknown1
Firman Yosef Rangga NeonbeniAffiliation unknown1
Risald RisaldAffiliation unknown1
Patricia Gertrudis ManekInstitut Teknologi Sepuluh Nopember1
Agus Zainal ArifinInstitut Teknologi Sepuluh Nopember1
Ahmad Wahyu RosyadiInstitut Teknologi Sepuluh Nopember1
Renest DanardonoInstitut Teknologi Sepuluh Nopember1
Nanik SuciatiInstitut Teknologi Sepuluh Nopember1
Ricky Eka PutraAffiliation unknown1
Michael Ndung'uAffiliation unknown1
Gigih Prasetyo CahyonoAffiliation unknown1
Ahmad Hifdhul AbrorUIN Sunan Ampel Surabaya1
Gama Wisnu FajariantoInstitut Teknologi Sepuluh Nopember1
Stendy Budi Hartono SakurPoliteknik Negeri Nusa Utara1
Ahmad Hayam BrilianAffiliation unknown1
Aditya BagusmulyaInstitut Teknologi Sepuluh Nopember1
Yunan Helmi MahendraInstitut Teknologi Sepuluh Nopember1
Nurseno Bayu AjiInstitut Teknologi Sepuluh Nopember1
Martini Dwi Endah SusantiInstitut Teknologi Sepuluh Nopember1
Adenuar PurnomoInstitut Teknologi Sepuluh Nopember1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://juti.if.its.ac.id/index.php/juti/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Soft Weighted Median Filter Method for Improved Image Segmentation with Noise</dc:title>
	<dc:creator xml:lang="en">Manek, Siprianus Septian</dc:creator>
	<dc:creator xml:lang="en">Tjandrasa, Handayani</dc:creator>
	<dc:description xml:lang="en">Soft Weighted Median Filter Method (SWMF) is one of the new methods for noise filtering in image processing. This method is used for two types of noise in images, there is fixed valued noise (FVN) and random valued noise (RVN). Fixed valued noise is a noise type with an unchanged value, it changes the pixel value of the image to the maximum and minimum values (0 and 255), while random valued noise is a noise type with a changed value. An example of fixed valued noise is salt &amp;amp; pepper noise, while for random valued noise can be exemplified as gaussian, poisson, speckle, and localvar noise.Based on previous research, SWMF method can be applied to all images with all kinds of noise (FVN and RVN) and able to reduce the noise well. This method has a higher PSNR value than other methods, especially for random valued noise types such as: gaussian, speckle, and localvar noise.In this study, we propose to examine the performance of the SWMF method further by comparing this method with other methods such as Median Filter, Mean Filter, Gaussian Filter, and Wiener Filter in an image segmentation process. The image segmentation process in this research is based on area detection using Top-Hat transform and Otsu thresholding and line detection using Sobel edge detection. The performance measurement process uses the calculation of sensitivity value, specificity, and accuracy on the image segmentation with the groundtruh image.The results show that Soft Weighted Median Filter method can improve the quality of image segmentation with the average accuracy of 95.70% by reducing fixed value noise and random valued noise in the images.</dc:description>
	<dc:publisher xml:lang="en">Institut Teknologi Sepuluh Nopember</dc:publisher>
	<dc:date>2018-10-09</dc:date>
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	<dc:identifier>10.12962/j24068535.v16i2.a721</dc:identifier>
	<dc:source xml:lang="en">JUTI: Jurnal Ilmiah Teknologi Informasi; Vol 16, No. 2, Juli 2018; 125-133</dc:source>
	<dc:source>2406-8535</dc:source>
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