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Please use this identifier to cite or link to this item: http://hdl.handle.net/11129/4819

Title: Median Filter Based Digital Image Restoration Using Joint Statistical Modeling
Authors: Ergün, Cem
Salih, Hankaw Qader
Eastern Mediterranean University, Faculty of Engineering, Dept. of Computer Engineering
Keywords: Computer Engineering
Image reconstruction--Computers--Imaging Systems
Image processing - Digital techniques
Computers--Digital Media--Graphics Applications
Imaging Systems
Image restoration
joint statistical modeling
image inpainting
image deblurring
noise removal
Issue Date: 2018
Publisher: Eastern Mediterranean University (EMU) - Doğu Akdeniz Üniversitesi (DAÜ)
Citation: Salih, Hankaw Qader. (2018). Median Filter Based Digital Image Restoration Using Joint Statistical Modeling. Thesis (M.S.), Eastern Mediterranean University, Institute of Graduate Studies and Research, Dept. of Computer Engineering, Famagusta: North Cyprus.
Abstract: Image restoration involves the reduction or complete removal of image degradation in an effort to enhance an image and recover its original form. One of the main methods of image restoration is Joint Statistical Modeling (JSM). This thesis proposes method for image restoration based on JSM and the statistical characterization of the nonlocal self-similarity and local smoothness of natural images. In an effort to improve the image restoration results through JSM, the proposed method involves the addition of a Switching Median Filter (SMF) to JSM and a Median Filter (MF) at the end of every iteration in the restoration process. Overall, the proposed image restoration method makes the following contributions: it establishes JSM in a domain for hybrid space-transformation; using JSM, it develops a new type of minimization function to be used in solving inverse problems in image processing; and JSM is developing a new rule-based in the Split Bregman method, which is intended to solve any prospective image problems related to a theoretical proof of convergence. The proposed method was experimentally tested for three kinds of image restoration: image deblurring, image inpainting (text removal), and the removal of mixed Gaussian and salt-and-pepper noise. The results of these experiments indicate that image restoration using the proposed method is a significant improvement compared to conventional JSM. Furthermore, the convergence of the proposed method was also considerably improved relative to JSM.
ÖZ: Resim onarma işlemi, resimdeki mevcut bozunumun azaltılması tamamen ortadan kaldırılması amacıyla yapılan iyileştirme ve asıl haline dönüştürme işlemidir. En başta gelen yöntemlerden bir taneside Ortak İstatiksel Model (OİM) yöntemidir. Bu tezde, resimlerin yerel olmayan özbenzeşlik ve yerel pürüzsüzlük istatiksel nitelendirilmesi ile OİM’e dayalı yeni bir resim onarma yöntemi sunulmuştur. Bu yöntemde OİM’den alınan sonuçları iyileştiremek amacıyla, OİM yöntemine anahtarlamalı ortancı süzgeci eklenerek onarma sürecinde ortancı süzgecin her bir iterasyonda kullanılması öngörülmüştür. Sonuç olarak, şu katkılar sağlanmıştır; OİM kullanarak resmi tersten işleme problemini çözmede yeni bir azaltma fonksiyonu geliştirilmiştir. Geliştirilen kural bazlı Split Bregman yöntemi ile OİM iyileştirilmiş her türlü olası tersten resim işleme problemlerine kuramsal bir yakınsama kanıtı sunulmuştur. Önerilen yöntem deneysel olarak üç ayrı resim onarma uygulamasında test edilmiştir; Bunlar, resim netleştirme, resim iç boyama (metin giderme) ve karışık Gauss ve tuz-ve-biber gürültüsünün kaldırılmasıdır. Yapılan deneylerin sonucuna göre resim onarmada önerilen yöntem ile anlamlı bir gelişme sağlanmıştır. Buna ek olarak önerilen yöntem ile yakınsama OİM’den daha iyi olmuştur.
Description: Master of Science in Computer Engineering. Thesis (M.S.)--Eastern Mediterranean University, Faculty of Engineering, Dept. of Computer Engineering, 2018. Supervisor: Asst. Prof. Dr. Cem Ergün
URI: http://hdl.handle.net/11129/4819
Appears in Collections:Theses (Master's and Ph.D) – Computer Engineering

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