Rifat Kurban

Dr. Rifat Kurban

Department of Computer Engineering, Abdullah Gül University, Kayseri, Turkey (Türkiye)
orcid

Research Focus & Metrics

Academic Career Length

21Years (2006-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
1 December 2010 in Expert Systems with Applications
Verified
Journal Article
Closed access
VAV. Aslantas
V. Aslantas
Rifat KurbanRifat Kurban
Rifat Kurban

1 October 2014 in Applied Soft Computing
Verified
Journal Article
Closed access
TKTuba Kurban
Tuba Kurban
PCPinar Civicioglu
Pinar Civicioglu
Rifat KurbanRifat Kurban
Rifat Kurban
EBErkan Besdok
Erkan Besdok

1 August 2009 in Optics Communications
Verified
Journal Article
Closed access
VAV. Aslantas
V. Aslantas
Rifat KurbanRifat Kurban
Rifat Kurban

15 August 2023 in Entropy
Verified
Journal Article
Open access
PDF file
Rifat KurbanRifat Kurban
Rifat Kurban
The separate analysis of images obtained from a single source using different camera settings or spectral bands, whether from one or more than one sensor, is quite difficult. To solve this problem, a single image containing all of the distinctive pieces of information in each source image is generally created by combining the images, a process called image fusion. In this paper, a simple and efficient, pixel-based image fusion method is proposed that relies on weighting the edge information associated with each pixel of all of the source images proportional to the distance from their neighbors by employing a Gaussian filter. The proposed method, Gaussian of differences (GD), was evaluated using multi-modal medical images, multi-sensor visible and infrared images, multi-focus images, and multi-exposure images, and was compared to existing state-of-the-art fusion methods by utilizing objective fusion quality metrics. The parameters of the GD method are further enhanced by employing the pattern search (PS) algorithm, resulting in an adaptive optimization strategy. Extensive experiments illustrated that the proposed GD fusion method ranked better on average than others in terms of objective quality metrics and CPU time consumption.

21 February 2023 in Neural Computing and Applications
Verified
Journal Article
Closed access
PDF file
SASevim Adige
Sevim Adige
Rifat KurbanRifat Kurban
Rifat Kurban
ADAli Durmus
Ali Durmus
EKErcan KARAKÖSE
Ercan KARAKÖSE

1 October 2021 in Engineering Applications of Artificial Intelligence
Verified
Journal Article
Closed access
Rifat KurbanRifat Kurban
Rifat Kurban
ADAli Durmus
Ali Durmus
EKErcan KARAKÖSE
Ercan KARAKÖSE