Analisis & Implementasi Image Denoising Menggunakan<br /> Metoda Bivariate Shrinkage Dengan Local Variance<br /> Estimation<br /> <br /> Analysis & Implementation of Image Denoising Using<br /> Bivariate Shrinkage With Local Variance Estimation<br
| Main Author: | Teguh Umbara |
|---|---|
| Format: | Bachelors |
| Terbitan: |
Universitas Telkom
, 2007
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| Subjects: | |
| Online Access: |
https://openlibrary.telkomuniversity.ac.id/pustaka/94084/analisis-amp-implementasi-image-denoising-menggunakan-br-metoda-bivariate-shrinkage-dengan-local-variance-br-estimation-br-br-analysis-amp-implementation-of-image-denoising-using-br-bivariate-shrinkage-with-local-variance-estimation-br.html |
Daftar Isi:
- ABSTRAKSI: Permasalahan wavelet thresholding pada image denoising adalah<br /> bagaimana menentukan nilai threshold yang tepat. Penggunaan metoda seperti<br /> Normalshrink untuk mencari nilai threshold bisa menyelesaikan permasalahan.<br /> Namun metoda Normalshrink mengasumsikan bahwa wavelet coeffient bersifat<br /> independent. Penggunaan metoda Bivariate Shrinkage Dengan Local Variance<br /> Estimation tetap mempertahankan sifat dependent dari wavelet coefficient<br /> sehingga bisa meningkatkan performansi image denoising. Performansi metoda<br /> ini dipengaruhi oleh ukuran windowsize pada saat perhitungan variance dari citra<br /> ternoise.<br /> Dalam Tugas Akhir ini telah dianalisis dan diimplementasikan image<br /> denoising menggunakan metode Bivariate Shrinkage Dengan Local Variance<br /> Estimation. Pengujian dilakukan terhadap berbagai ukuran windowsize sehingga<br /> diketahui pengaruhnya terhadap PSNR hasil denoising dan waktu komputasi<br /> proses denoising. Noise yang digunakan dalam pengujian adalah additive<br /> gaussian noise, additive laplacian noise, dan impulsive noise yang dibangkitkan<br /> melalui suatu noise generator.<br /> Dari hasil percobaan didapatkan bahwa metoda Bivariate Shrinkage<br /> Dengan Local Variance Estimation mendapatkan PSNR hasil denoising yang<br /> lebih baik sekitar 0.01~0.5 dB terhadap Bivariate Shrinkage dan 0.05~1.5 dB<br /> terhadap Normalshrink. Waktu komputasi proses denoising metoda ini<br /> dipengaruhi oleh ukuran windowsize, semakin besar windowsize maka semakin<br /> tinggi waktu komputasi proses denoising.Kata Kunci : wavelet thresholding, image denoising, bivariate shrinkage, local variance estimation, windowsize.ABSTRACT: The main problem in image denoising using wavelet thresholding is how<br /> to obtain the effective threshold value. The Normalshrink usage to obtain this<br /> value can be accomplish the problem. But Normalshrink assumes that wavelet<br /> coefficients are independent each other. Bivariate Shrinkage With Local Variance<br /> Estimation usage keeps the dependent between wavelet coefficient so can improve<br /> the performance of image denoising. The performance of this method is<br /> influenced by windowsize in noised image’s marginal variance measurement<br /> In this Final Project, it has been analysed and implemented the used of<br /> Bivariate Shrinkage With Local Variance Estimation method for image denoising.<br /> Testing phase is toward to varying windowsize so the influences in denoising<br /> PSNR’s result and computational time will be known. The noise which is used in<br /> testing phase are additive gaussian noise, additive laplacian noise and impulsive<br /> noise which is generated by noise generator.<br /> From the experiment result, Bivariate Shrinkage With Local Variance<br /> Estimation method have better PSNR’s denoising result about 0.01~0.5 dB toward<br /> to Bivariate Shrinkage and 0.05~1.5 dB toward to Normalshrink. Denoising<br /> computational time of this method is influenced by windowsize, bigger<br /> windowsize needs bigger denoising computational time.Keyword: wavelet thresholding, image denoising, bivariate shrinkage, local variance estimation, windowsize.