LAUSR

Synthetic aperture radar (SAR) images play an increasingly important role in ocean environmental monitoring and research. However, SAR images are inherently corrupted by speckle noise. SAR ocean images have some unique characteristics. The signatures of ocean phenomena in SAR images mainly exhibit as stripe or plaque shaped features. These features typically have a high degree of self-similarity or redundancy. The nonlocal means (NLM) method can measure the structural similarity between different image patches and take advantage of redundant information in images. Considering that the NLM algorithm is computationally intensive and time-consuming, an accelerated NLM algorithm for SAR ocean image despeckling is proposed in this paper. A method is used to discriminate between texture and flat pixels in SAR images. Large similarity and search windows are used on texture pixels, whereas small similarity and search windows are used on flat pixels. Furthermore, the improved NLM algorithm is accelerated by a graphic processing unit (GPU) based on the compute unified device architecture (CUDA) parallel computation framework. The computational efficiency is improved by approximately 200 times.