Ozcan, CanerSen, BahaNar, Fatih2024-09-292024-09-292016978-1-5090-1679-2https://hdl.handle.net/20.500.14619/843724th Signal Processing and Communication Application Conference (SIU) -- MAY 16-19, 2016 -- Zonguldak, TURKEYSpeckle 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.trinfo:eu-repo/semantics/closedAccesssynthetic aperture radarspeckle noisetotal variationparallel programmingCUDAConference Object13321329WOS:000391250900309N/A