Tuz ve Biber Gürültü Kaldırma için Uyarlamalı Cesáro Ortalama Filtresi

Abstract

Bu çalışmada, bir tuz ve biber gürültü (SPN) kaldırma yöntemi, yani Uyarlamalı Cesáro Ortalama Filtresi (ACmF) öneriyoruz ve bazı temel kavramları veriyoruz. Ardından, ACmF’yi gürültü yoğunluğu %10 ile %90 arasında değişen çeşitli test görüntülerine uyguluyoruz: 15 geleneksel test görüntüsü (Baboon, Boat, Bridge, Cameraman, Elaine, Flintstones, Hill, House, Lake, Lena, Living Room, Parrot, Peppers, Pirate, and Plane) ve TESTIMAGES veri tabanında verilen 40 test görüntüsü. Daha sonra, ACmF'yi Uyarlamalı Ağırlıklı Ortalama Filtresi (AWMF), Farklı Uygulamalı Medyan Filtresi (DAMF) ve Gürültü Uyarlamalı Bulanık Anahtarlama Medyan Filtresi (NAFSMF) gibi gelişmiş yöntemlerle karşılaştırıyoruz. Pik Sinyal Gürültü Oranı (PSNR) ve Yapısal Benzerlik (SSIM) sonuçları, ACmF'nin yukarıda belirtilen yöntemlerden daha iyi performans sergilediğini göstermektedir. Ayrıca, bu algoritmaların çalışma zamanlarını da karşılaştırıyoruz. Bu çalışma süresi sonuçları ACmF'nin DAMF dışındaki yöntemleri geride bıraktığını gösteriyor. Sonunda daha fazla araştırmaya olan ihtiyacı tartışıyoruz.

Keywords

• Tuz ve biber gürültüsü,gürültü kaldırma,,lineer olmayan fonksiyonlar,,Cesáro Ortalama

Adaptive Cesáro Mean Filter for Salt-and-Pepper Noise Removal

Abstract

In this study, we propound a salt-and-pepper noise (SPN) removal method, i.e. Adaptive Cesáro Mean Filter (ACmF), and provide some of its basic notions. We then apply ACmF to several test images whose noise densities range from 10% to 90%: 15 traditional test images (Baboon, Boat, Bridge, Cameraman, Elaine, Flintstones, Hill, House, Lake, Lena, Living Room, Parrot, Peppers, Pirate, and Plane) and 40 test images, provided in the TESTIMAGES Database. Afterwards, we compare ACmF with the state-of-art methods, such as Adaptive Weighted Mean Filter (AWMF), Different Applied Median Filter (DAMF), and Noise Adaptive Fuzzy Switching Median Filter (NAFSMF). The results by The Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) show that ACmF performs better than the methods mentioned above. Moreover, we also compare the running time data of these algorithms. These results show that ACmF outperforms the methods except for DAMF. We finally discuss the need for further research.

Keywords

• Salt-and-pepper noise,noise removal,,non-linear functions,image denoising,,Cesáro mean

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