Ozcan, C.Sen, B.Nar, F.2024-09-292024-09-292016978-150901679-2https://doi.org/10.1109/SIU.2016.7495993https://hdl.handle.net/20.500.14619/926724th Signal Processing and Communication Application Conference, SIU 2016 -- 16 May 2016 through 19 May 2016 -- Zonguldak -- 122605Speckle 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.trinfo:eu-repo/semantics/closedAccessCUDAparallel programmingspeckle noisesynthetic aperture radartotal variationConference Object10.1109/SIU.2016.74959932-s2.0-849828592671332N/A1329