An approximate arithmetic progression approach for image filtering technique
Image have a significant importance in many fields in human life such as, in medicine, photography, biology, astronomy, industry and defense. Thus, it attracts the attention of large number of researchers, among them those interested in preserving the image features from any factors that may reduce...
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| Format: | Thesis |
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| Language: | English |
| Subjects: | |
| Online Access: | http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78014/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78014/2/Full%20text.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78014/4/Bilal%20Charmouti.pdf |
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| Summary: | Image have a significant importance in many fields in human life such as, in medicine, photography, biology, astronomy, industry and defense. Thus, it attracts the attention of large number of researchers, among them those interested in preserving the image
features from any factors that may reduce the image quality. Removing noise from the image by retaining the features of this treated image remains a standing challenge for the researchers in image processing field. Therefore, this study is carried out to propose and implement a new filtering technique for removing Salt & Pepper noise from the digital
image. This technique permits the narrowing of the gap between the original and the
restored images, visually and quantitatively by adopting the mathematical concept
(arithmetic progression) due to its ability in modelling the variation of pixels intensity in
the image. The principle of the proposed filtering technique relies on the precision, where
it keeps the uncorrupted pixels by using effective noise detection and converts the
corrupted pixels by replacing them with other closest pixels from the original image at
lower cost and with more simplicity. The results illustrate that the proposed filtering
technique gives an acceptable performance compared to the existing methods whether
visually or quantitatively with peak signal-to-noise ratio (PSNR) and mean squared error
(MSE). |
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