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Ters mesafe ağırlıklandırma yöntemiyle bilgisayarlı tomografi imgeleri için yeni bir süper çözünürlük yaklaşımı

Bu çalışmada ters mesafe ağırlıklandırma yöntemi ve histogram eşitleme yöntemlerinin bütünleşikkullanılması ile oluşturulan bir tekli imge süper çözünürlük yaklaşımı önerilmiştir. Yapılan çalışmadaimgelerin boyutlarının artırılması sonucu oluşacak detay kayıplarının en aza indirgenmesi hedeflenmiştir.Önerilen yaklaşımda, ters mesafe ağırlıklandırma yöntemi ile imgeye ait kenar bilgileri başarı ile korunurken,piksellere ait parlaklık değerleri genel histogram eşitleme sayesinde gerçek imgeye benzetilmiştir.Bilgisayarlı tomografi imgelerinden oluşan bir veri tabanı kullanılarak yaklaşımın başarımı test edilmiştir.Elde edilen sonuçlar, literatürde kullanılan çeşitli süper çözünürlük yöntemleri ile detaylı bir şekildekarşılaştırılmıştır. Yöntemlerin başarımları karşılaştırılırken, korelasyon katsayısı, tepe sinyal gürültü oranı,yapısal benzerlik indeksi ve Pratt’ın başarım ölçüsünden faydalanılmıştır.

A novel super-resolution approach for computed tomography images by inverse distance weighting method

In this study, a single image super-resolution approach, which is an integrated use of inverse distance weighting and histogram equalization methods, is proposed. It is aimed to reduce the detail loss which will be the result of increasing the dimensions of the images. In the proposed approach, while the edge information of the image is successfully preserved by the inverse distance weighting method, the brightness values of the pixels are approximated to the true image through general histogram equalization. The performance of the approach has been tested using a computed tomography database. The results obtained were compared in detail with various super-resolution methods available in the literature. When comparing the performance of the method, correlation coefficient, peak signal to noise ratio, structural similarity index and Pratt's figure of merit were used.

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