2 boyutlu sıfıra çeken LMS algoritmasıyla görüntü iyileştirme

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

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.

Keywords:

LMS, Image restoration, Sparse signals,

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