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Journal of Information Technology
Article . 2022 . Peer-reviewed
License: CC BY
Data sources: Crossref
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Kinerja Penapisan Gaussian dan Median Dalam Pelembutan Citra

Authors: Gogor C. Setyawan; Maya Putri Nawansari;

Kinerja Penapisan Gaussian dan Median Dalam Pelembutan Citra

Abstract

Intisari - Metode Penapis Gaussian dan Penapis Median merupakan dua dari beberapa metode dalam pelembutan citra (smoothing image). Kedua metode tersebut digunakan untuk memperbaiki kualitas citra. Efek dari pelembutan citra adalah citra menjadi blur. Penapis Gaussian adalah penapis blur yang menempatkan warna transisi yang signifikan dalam sebuah citra, kemudian membuat warna-warna pertengahan untuk menciptakan efek lembut pada sisi-sisi sebuah citra. Untuk Penapis Median ini, data yang digunakan untuk menghitung Median terdiri dari kumpulan data yang ganjil. Hal ini disebabkan oleh jumlah data yang ganjil diharapkan piksel yang akan diproses dapat berada ditengah. Pada Penapis Median digunakan matrik berdimensi NxN. Dari matrik tersebut, kemudian data yang ada diurutkan dan dimasukkan dalam sebuah matrik berukuran 1X (NxN). Hal ini berguna untuk mempermudah menemukan median dari kumpulan data yang telah urut tersebut. Hasil perbandingan Penapis Gaussian dan Penapis Median Sesuai nilai PSNR (Peak Signal to Noise), dimana semakin besar nilai PSNR maka semakin baik hasil rekonstruksi gambar. Dari 30 gambar uji yang digunakan dalam penelitian ini, dapat disimpulkan bahwa dalam kasus ini metode Penapis Gaussian lebih baik dari pada Penapis Median dalam pelembutan citra.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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