An Edge Preserving Image Denoising Framework Based on Statistical Edge Detection and Bilateral Filter

Öz

The task of reducing noise from an image is known as image denoising. Although there are various methods and algorithms proposed in the literature, the methods still have limitations. The approaches generally either fail to reduce noise adequately or cause to be lost while effectively reducing noise. Conventional methods have poor performance when considering the success of preserving region boundaries and small structures. Conversely, modern techniques are more effective to smooth images without over smoothing edge details. To address these deficiencies and benefits, in this paper, we aim to develop a framework, which is capable of detecting whether a pixel is a part of edges or textures in an image so the framework can decide which filter should be used depending on region information. The Rank Order Test Method is used to detect image edges. In this way, we determine both which neighbors should be included to build a filter mask in the calculation for each pixel and which filter method should be implemented. We have compared the performance of Bilateral Filter-based methods. Experiments demonstrate that the proposed framework outperforms in terms of both PSNR, SSIM and visual perception for the noise with standard deviations 10, 30, 50. While the average PSNR value was 30.33 DB for the proposed model, the method with the closest result achieved an average score of 28.33 DB.

Anahtar Kelimeler

• Bilateral filter
• image denoising
• region growing segmentation
• statistical test

İstatistiksel Kenar Algılama ve Bilateral Filtreye Dayalı Kenar Korumalı Gürültü Giderme Yöntemi

Öz

Bir görüntüdeki gürültüyü azaltma işlemi, gürültü giderme olarak adlandırılır. Literatürde önerilen çeşitli yöntemler ve algoritmalar olmasına rağmen, yöntemlerin hala sınırlamaları bulunmaktadır. Yaklaşımlar genellikle ya gürültüyü yeterince azaltmakta başarısız olur ya da gürültüyü etkili bir şekilde azaltırken görüntünün kaybolmasına neden olur. Bölge sınırlarını ve küçük yapıları korumanın başarısı göz önüne alındığında, geleneksel yöntemlerin performansı düşüktür. Tersine, modern teknikler, kenar ayrıntılarını aşırı yumuşatmadan görüntüleri düzeltmek için daha etkilidir. Bu eksiklikleri ve faydaları göz önünde bulundurarak, bu çalışmada, bir pikselin bir görüntüdeki kenarların mı yoksa dokuların bir parçası mı olduğunu tespit edebilen ve böylece çerçevenin bölge bilgisine bağlı olarak hangi filtrenin kullanılması gerektiğine karar verebilen bir çerçeve geliştirilmesi amaçlanmıştır. Sıralama Testi Yöntemi, görüntü kenarlarını tespit etmek için kullanılır. Bu sayede her piksel için yapılan hesaplamada filtre maskesi oluşturmak için hangi komşuların dâhil edilmesi gerektiği hem de hangi filtre yönteminin uygulanması gerektiği belirlenmiştir. Çalışmada Bilateral Filtre tabanlı yöntemlerin performanslarını karşılaştırılmıştır Deneyler, 10,30 ve 50 standart sapmalara sahip gürültüler için önerilen çerçevenin PSNR, SSIM ve görsel algı açısından daha iyi performans sağladığını göstermektedir. Ortalama PSNR değeri 30.33 DB iken, en yakın sonuca sahip olan yöntem 28.33 DB ortalama puan elde etmiştir.

Anahtar Kelimeler

• Bilateral filtre
• gürültü giderme
• alan büyütmeli bölütleme
• istatistiksel test

Kaynakça

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