Caner Ozcan

Sparsity-Driven Despeckling for SAR Images

Speckle noise inherent in synthetic aperture radar (SAR) images seriously affects the result of various SAR image processing tasks such as edge detection and segmentation. Thus, speckle reduction is critical and is used as a preprocessing step for smoothing homogeneous regions while preserving features such as edges and point scatterers. Although state-of-the-art methods provide better despeckling compared with conventional methods, their resource consumption is higher. In this letter, a sparsity-driven total-variation (TV) approach employing l0-norm, fractional norm, or l1-norm to smooth homogeneous regions with minimal degradation in edges and point scatterers is proposed. Proposed method, sparsity-driven despeckling (SDD), is capable of using different norms controlled by a single parameter and provides better or similar despeckling compared with the state-of-the-art methods with shorter execution times. Despeckling performance and execution time of the SDD are shown using synthetic and real-world SAR images.

Fast feature preserving despeckling

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.

GPU efficient SAR image despeckling using mixed norms

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. Therefore, speckle reduction is generally used as a first step which has to smooth out homogeneous regions while preserving edges and point scatterers. Traditional speckle reduction methods are fast and their memory consumption is insignificant. However, they are either good at smoothing homogeneous regions or preserving edges and point scatterers. State of the art despeckling methods are proposed to overcome this trade-off. However, they introduce another trade-off between denoising quality and resource consumption, thereby higher denoising quality requires higher computational load and/or memory consumption. In this paper, a local pixel-based total variation (TV) approach is proposed, which combines l2-norm and l1-norm in order to improve despeckling quality while keeping execution times reasonably short. Pixel-based approach allows efficient computation model with relatively low memory consumption. Their parallel implementations are also more efficient comparing to global TV approaches which generally require numerical solution of sparse linear systems. However, pixel-based approaches are trapped to local minima frequently hence despeckling quality is worse comparing to global TV approaches. Proposed method, namely mixed norm despeckling (MND), combines l2-norm and l1-norm in order to improve despeckling performance by alleviating local minima problem. All steps of the MND are parallelized using OpenMP on CPU and CUDA on GPU. Speckle reduction performance, execution time and memory consumption of the proposed method are shown using synthetic images and TerraSAR-X spot mode SAR images.

Fast text classification with Naive Bayes method on Apache Spark

The increase in the number of devices and users online with the transition of Internet of Things (IoT), increases the amount of large data exponentially. Classification of ascending data, deletion of irrelevant data, and meaning extraction have reached vital importance in todays standards. Analysis can be done in various variations such as Classification of text on text data, analysis of spam, personality analysis. In this study, fast text classification was performed with machine learning on Apache Spark using the Naive Bayes method. Spark architecture uses a distributed in-memory data collection instead of a distributed data structure presented in Hadoop architecture to provide fast storage and analysis of data. Analyzes were made on the interpretation data of the Reddit which is open source social news site by using the Naive Bayes method. The results are presented in tables and graphs

Sparsity-driven despeckling method with low memory usage

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.

Total variation based 3D skull segmentation

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.

Early-exit optimization using mixed norm despeckling for SAR images

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.