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Öğe Early-exit optimization using mixed norm despeckling for SAR images(Institute of Electrical and Electronics Engineers Inc., 2015) Özcan, C.; Sen, B.; Nar, F.Speckle noise which is inherent to Synthetic Aperture Radar (SAR) imaging obstructs various image exploitation tasks such as edge detection, segmentation, change detection, and target recognition. Speckle reduction is generally used as a first step which has to smooth out homogeneous regions while preserving edges and point scatterers. In remote sensing applications, efficiency of computational load and memory consumption of despeckling must be improved for SAR images. In this paper, an early-exit total variation approach is proposed and this approach combines the l1-norm and the l2-norm in order to improve despeckling quality while keeping execution times of algorithm reasonably short. Speckle reduction performance, execution time and memory consumption are shown using spot mode SAR images. © 2015 IEEE.Öğe Fast feature preserving despeckling(IEEE Computer Society, 2014) Ozcan, C.; Sen, B.; Nar, F.Synthetic Aperture Radar (SAR) images contain high amount of speckle noise which causes edge detection, shape analysis, classification, segmentation, change detection and target recognition tasks become more difficult. To overcome such difficulties, smoothing of homogenous regions while preserving point scatterers and edges during speckle reduction is quite important. Besides, due to huge size of SAR images in remote sensing applications efficiency of computational load and memory consumption must be further improved. In this paper, a parallel computational approach is proposed for the Feature Preserving Despeckling (FPD) method which is chosen due to its success in speckle reduction. Speckle reduction performance, execution time and memory consumption of the proposed Fast FPD (FFPD) method is shown using spot mode SAR images. © 2014 IEEE.Öğe Scattered data estimation on medical images for cranioplasty applications(IEEE Computer Society, 2014) Atasoy, F.; Nar, F.; Sen, B.; Ferat, M.Cranioplasty is a surgical operation to repair hole or defects on skull. 3 dimensional computed tomography (CT) images are used for automatic determination of the shape of implant which is used for repairing defect. The designing implant by mathematical model and manufacturing it before operation lowers the operation cost. In this paper, previous studies are examined, applications are realized by radial basis functions (RBF) and insufficient sections of previous studies are revealed. © 2014 IEEE.Öğe Sparsity-driven despeckling method with low memory usage(Institute of Electrical and Electronics Engineers Inc., 2016) Ozcan, C.; Sen, B.; Nar, F.Speckle noise which is inherent to Synthetic Aperture Radar (SAR) imaging makes it difficult to detect targets and recognize spatial patterns on earth. Thus, despeckling is critical and used as a preprocessing step for smoothing homogeneous regions while preserving features such as edges and point scatterers. In this study, a low-memory version of the previously proposed sparsity-driven despeckling (SDD) method is proposed. All steps of the method are parallelized using OpenMP on CPU and CUDA on GPU. Execution time and despeckling performance are shown using real-world SAR images. © 2016 IEEE.Öğe Total variation based 3D skull segmentation(Institute of Electrical and Electronics Engineers Inc., 2016) Atasoy, F.; Sen, B.; Nar, F.; Ozcan, C.; Bozkurt, I.Segmentation is widely used for determining tumor and other lesions and classifying tissues for various analysis purposes in medical images. However, being an ill-posed problem, there is no single segmentation method which can perform successfully for all kind of data. In this study, a novel total variation (TV) based skull segmentation method is proposed. Skull segmentation performance of the proposed method is shown using computed tomography (CT) images. © 2016 IEEE.
