Derin Öğrenme Tabanlı Hibrit Model ile Dijital Görüntülerde Periyodik Gürültülerin Giderilmesi

Öz

Bu çalışma, dijital görüntülerde sıkça karşılaşılan periyodik gürültülerin giderilmesine yönelik derin öğrenme tabanlı hibrit bir yöntem önermektedir. Periyodik gürültü, görüntü kalitesini düşürerek analiz ve yorum süreçlerini güçleştirir. Geleneksel filtreleme yöntemlerinin bu tür gürültüler üzerinde sınırlı başarı sağlaması, daha etkili tekniklerin geliştirilmesini gerekli kılmıştır. Önerilen yöntemde, ilk aşamada Fourier dönüşümü yardımıyla frekans alanındaki gürültü tepe noktaları belirlenmiş ve bu bölgeler çentik filtresi ile istatistiksel olarak temizlenmiştir. İkinci aşamada ise DnCNN (Denoising Convolutional Neural Network) modeli uygulanarak görüntüde kalan gürültü bileşenleri giderilmiş ve detay kaybı en aza indirilmiştir. Modelin eğitimi, sentetik olarak oluşturulan ve farklı türde periyodik gürültülerin uygulandığı veri setleri üzerinde gerçekleştirilmiştir. Elde edilen sonuçlar, PSNR ve SSIM metrikleriyle değerlendirilmiş; hibrit yöntemin en yüksek 38,15 dB PSNR ve 0,96 SSIM değerlerine ulaştığı ve yalnızca gürültüyü azaltmakla kalmayıp, aynı zamanda görüntülerin yapısal bütünlüğünü koruduğu görülmüştür. Önerilen model, geleneksel yöntemlere kıyasla daha tutarlı ve başarılı sonuçlar üretmiş, farklı periyodik gürültü türlerinde yüksek performans sergilemiştir. Bu yönüyle çalışma, görüntü işleme alanında derin öğrenme destekli periyodik gürültü giderimi için etkili ve uygulanabilir bir çözüm sunmakta, gelecekteki araştırmalar için de güçlü bir temel oluşturmaktadır.

Anahtar Kelimeler

• Periyodik gürültü
• Gürültü Giderme Yöntemleri
• Derin Öğrenme
• DnCNN

Mitigating Periodic Noise in Digital Images Using a Deep Learning-Based Hybrid Model

Öz

This study proposes a deep learning-based hybrid method for the removal of periodic noise frequently encountered in digital images. Periodic noise degrades image quality and complicates analysis and interpretation processes. The limited success of traditional filtering techniques in handling such noise highlights the need for more effective solutions. In the proposed approach, the first stage involves identifying noise peak points in the frequency domain using Fourier Transform, followed by statistical denoising with a notch filter. In the second stage, the DnCNN (Denoising Convolutional Neural Network) model is applied to eliminate residual noise components and minimize detail loss. The model was trained on a dataset consisting of synthetically generated images containing various types of periodic noise. The performance of the method was evaluated using PSNR and SSIM metrics. The findings indicate that the hybrid method achieved maximum values of 38.15 dB PSNR and 0.96 SSIM, demonstrating that it not only effectively mitigates noise but also maintains the structural integrity of the images. Compared to traditional methods, the proposed model achieved more consistent and successful outcomes, showing high performance across different types of periodic noise. In this context, the study offers an effective and practical approach for removing periodic noise in image processing and establishes a solid basis for subsequent research on deep learning-based denoising methods.

Anahtar Kelimeler

• Periodic noise
• Noise removal methods
• Deep Learning
• DnCNN.

Kaynakça

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