Deblurring Medical Images Using a New Grünwald-Letnikov Fractional Mask
Yükleniyor...
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IOS Press BV
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this paper, we propose a novel image deblurring approach that utilizes a new mask based on the Grünwald-Letnikov fractional derivative. We employ the first five terms of the Grünwald-Letnikov fractional derivative to construct three masks corresponding to the horizontal, vertical, and diagonal directions. Using these matrices, we generate eight additional matrices of size 5 × 5 for eight different orientations: kπ4 , where k = 0, 1, 2, . . ., 7. By combining these eight matrices, we construct a 9 × 9 mask for image deblurring that relates to the order of the fractional derivative. We then categorize images into three distinct regions: smooth areas, textured regions, and edges, utilizing the Wakeby distribution for segmentation. Next, we determine an optimal fractional derivative value tailored to each image category to effectively construct masks for image deblurring. We applied the constructed mask to deblur eight brain images affected by blur. The effectiveness of our approach is demonstrated through evaluations using several metrics, including PSNR, AMBE, and Entropy. By comparing our results to those of other methods, we highlight the efficiency of our technique in image restoration. © 2024 Vilnius University.
Açıklama
Anahtar Kelimeler
Gradient Matrix, Grünwald-Letnikov Fractional Derivatives, Wakeby Distribution
Kaynak
Informatica (Netherlands)
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
35
Sayı
4
Künye
Satvati, M. A., Lakestani, M., Khamnei, H. J., & Allahviranloo, T. (2024). Deblurring Medical Images Using a New Grünwald-Letnikov Fractional Mask. Informatica, 35(4), 817-836.
