2 boyutlu sıfıra çeken LMS algoritmasıyla görüntü iyileştirme

Yıl 2019, Cilt: 25 Sayı: 5, 539 - 545, 21.10.2019

Gülden Eleyan Muhammed Salman

Öz

Bu yazıda, iki boyutlu
en küçük kare algoritmasının (2D-LMS) maliyet fonksiyonuna seyrekliği farkeden
l₁-norm ceza terimi yükleyen yeni bir 2D sıfıra çeken en küçük
ortalama kare (ZALMS) uyarlamalı filtreyi önermekteyiz. 2D-LMS ve BM3D
algoritmaları ile karşılaştırmalar hem seyrek hem de seyrek olmayan
görüntülerde yürütülmüştür. Simülasyon sonuçları, önerilen algoritmanın hem
yatay hem de dikey doğrultuda filtre katsayılarının güncellenmesinde iyi
yeteneklere sahip olduğunu göstermiştir ve performansı düşük hesaplama zamanına
sahip 2D-LMS algoritması ile aynı/daha iyidir. Ancak 2D-ZALMS, BM3D
algoritmasından daha iyi performans göstermektedir.

Anahtar Kelimeler

LMS, Görüntü iyileştirme, Seyrek sinyaller

Image denoising with two-dimensional zero attracting LMS algorithm

Öz

In this paper, we propose a new two-dimensional
(2D) zero-attracting least-mean-square (ZALMS) adaptive filter by imposing a
sparsity aware l₁-norm penalty term into the cost function of the
2D-LMS algorithm. Comparisons with 2D-LMS and BM3D algorithms were conducted
both on sparse and non-sparse images. The carried-out simulations show that the
proposed algorithm has good capabilities in updating the filter coefficients
along both horizontal and vertical directions, and its performance is similar
with the 2D-LMS algorithm with lower computation time. But 2D-ZALMS performs
better than BM3D algorithm.

Anahtar Kelimeler

LMS, Image restoration, Sparse signals

Kaynakça

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