Despeckling of SAR images using GPU based on 3D-MAP estimation

Gibran H. Aranda-Bojorges; Beatriz P. Gracia-Salgado; Volodymyr I. Ponomaryov; Oscar Lopez-Garcia; Rogelio Reyes-Reyes

doi:10.1117/12.2618084

27 May 2022 Despeckling of SAR images using GPU based on 3D-MAP estimation

Gibran H. Aranda-Bojorges, Beatriz P. Gracia-Salgado, Volodymyr I. Ponomaryov, Oscar Lopez-Garcia, Rogelio Reyes-Reyes

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Proceedings Volume 12102, Real-Time Image Processing and Deep Learning 2022; 121020K (2022) https://doi.org/10.1117/12.2618084
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

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Abstract

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.

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Gibran H. Aranda-Bojorges, Beatriz P. Gracia-Salgado, Volodymyr I. Ponomaryov, Oscar Lopez-Garcia, and Rogelio Reyes-Reyes "Despeckling of SAR images using GPU based on 3D-MAP estimation", Proc. SPIE 12102, Real-Time Image Processing and Deep Learning 2022, 121020K (27 May 2022); https://doi.org/10.1117/12.2618084

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