| Zha Guozhen,Xu Dewei,Yang Yanming,Song Xin'gai,Zhong Fuhuang. 2019. An accelerated nonlocal means algorithm for synthetic aperture radar ocean image despeckling. Acta Oceanologica Sinica, 38(11):140－148 |
| An accelerated nonlocal means algorithm for synthetic aperture radar ocean image despeckling |
| 一种快速非局部平均合成孔径雷达海洋图像斑点噪声抑制方法 |
| Received:June 24, 2018 |
| DOI：10.1007/s13131-019-1504-5 |
| Key words:synthetic aperture radar|speckle noise|ocean|nonlocal means method|compute unified device architecture |
| 中文关键词: 合成孔径雷达,斑点噪声,海洋,非局部平均方法,计算统一设备并行架构 |
| 基金项目:The Scientific Research Foundation of Third Institute of Oceanography, SOA under contract No. 2015008; the National Natural Science Foundation of China under contract No. 61601132. |
| Author Name | Affiliation | E-mail | | Zha Guozhen | Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China | myfirstone@126.com | | Xu Dewei | Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China | | | Yang Yanming | Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China | | | Song Xin'gai | National Satellite Ocean Application Service, Beijing 100081, China | | | Zhong Fuhuang | Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China | |
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| Abstract: |
| 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. |
| 中文摘要: |
| 合成孔径雷达在海洋环境监测和海洋研究中扮演着越来越重要的角色。受其成像机制的影响，合成孔径雷达图像总是受到斑点噪声的污染。斑点噪声的存在会增大目标识别、跟踪和分类的难度，也会降低雷达信号的信噪比。合成孔径雷达海洋图像具有一些特殊的性质：海洋现象在雷达图像中主要呈现为条带状或斑块状的结构。这些条带状或斑块状的结构呈现出高度的自相似性或信息冗余。非局部平均方法能够衡量图像中不同图像块之间纹理结构的相似性，并利用图像的自相似性对图像进行去噪。但非局部平均去燥方法存在计算量巨大、计算耗时长的缺点，这几乎限制了其实际应用。本文采用一种自适应方法将雷达图像中的像素点区分为纹理区像素点和平坦区像素点。对纹理区像素点，采用较大的相似窗和搜索窗，对平坦区像素点，采用较小的相似窗和搜索窗，从而提高计算速度。进一步，本文基于计算统一设备并行架构（CUDA）技术，利用计算机图形处理器（GPU）对前述算法进行并行加速。与经典非局部平均算法相比，加速后算法的计算效率提高了200倍。 |
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