TY - GEN
T1 - Despeckling of SAR Images Using GPU Based on 3D-MAP Estimation
AU - Aranda-Bojorges, Gibran H.
AU - Gracia-Salgado, Beatriz P.
AU - Ponomaryov, Volodymyr I.
AU - Lopez-Garcia, Oscar
AU - Reyes-Reyes, Rogelio
N1 - Publisher Copyright:
© 2022 SPIE.
PY - 2022
Y1 - 2022
N2 - This paper proposes a parallel scheme for suppressing speckle noise in SAR images. The designed technique is based on forming 3D arrays of a clustered image by areas and using Maximum a Posteriori (MAP) estimation, where the a priori information is obtained by the Discrete Wavelet Transformation (DWT), improving the despeckling quality. Moreover, a variant of the bilateral filter is used as a post-processing stage to recover and enhance edges’ quality after the filtering procedure. The proposed scheme was implemented in serial and two parallel versions. The first one uses OpenMP to parallelize over a multi-core CPU, and the second utilizes CUDA to be executed in a GPU. Experimental results have demonstrated that the framework guarantees a good despeckling performance on SAR images obtained from the TerraSAR-X database, considering objective quality criteria such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Edge Preservation Index (EPI). Furthermore, the parallel implementations’ simulation results present their efficiency for a real-time environment.
AB - This paper proposes a parallel scheme for suppressing speckle noise in SAR images. The designed technique is based on forming 3D arrays of a clustered image by areas and using Maximum a Posteriori (MAP) estimation, where the a priori information is obtained by the Discrete Wavelet Transformation (DWT), improving the despeckling quality. Moreover, a variant of the bilateral filter is used as a post-processing stage to recover and enhance edges’ quality after the filtering procedure. The proposed scheme was implemented in serial and two parallel versions. The first one uses OpenMP to parallelize over a multi-core CPU, and the second utilizes CUDA to be executed in a GPU. Experimental results have demonstrated that the framework guarantees a good despeckling performance on SAR images obtained from the TerraSAR-X database, considering objective quality criteria such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Edge Preservation Index (EPI). Furthermore, the parallel implementations’ simulation results present their efficiency for a real-time environment.
KW - CPU-multicore
KW - CUDA
KW - GPU processing
KW - Parallel computing
KW - SAR images
KW - Speckle reduction
UR - http://www.scopus.com/inward/record.url?scp=85135700064&partnerID=8YFLogxK
U2 - 10.1117/12.2618084
DO - 10.1117/12.2618084
M3 - Contribución a la conferencia
AN - SCOPUS:85135700064
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Real-Time Image Processing and Deep Learning 2022
A2 - Kehtarnavaz, Nasser
A2 - Carlsohn, Matthias F.
PB - SPIE
T2 - Real-Time Image Processing and Deep Learning 2022
Y2 - 6 June 2022 through 12 June 2022
ER -