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Hybrid denoising algorithm of NSCT and improved NL-means method to SAR images

机译:NSCT的混合降噪算法和改进的NL-means方法合成SAR图像

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To efficiently preserve tiny details and sharpness information of synthetic aperture radar (SAR) images while clearly remove the speckles, a despeckling method is proposed in this letter. Firstly, the SAR image is creatively separated to two parts: the texture region and flat region by using the decomposition with non-subsampled contourlet transform (NSCT) and mask estimation iteration algorithm. secondly, in the texture region, a new method of integrating the non-local means (NL-means) with block matching is used to preserve the sharpness and tiny details of SAR images; finally, a big searching window is utilized only in the flat region to remove the noise to a great extent. The experimental results show that both the visual quality and evaluation index of the proposed method outperform the traditional three methods: enhance Lee filtering (ELF), the bilateral Filtering (BF) and the improved NL-means.
机译:为了有效地保留合成孔径雷达(SAR)图像的微小细节和清晰度信息,同时清晰地去除斑点,本文提出了一种去斑点方法。首先,利用非下采样轮廓波变换(NSCT)分解和掩模估计迭代算法,将SAR图像创造性地分为纹理区域和平坦区域两部分。其次,在纹理区域中,采用了一种将非局部均值(NL-means)与块匹配相结合的新方法,以保留SAR图像的清晰度和微小细节。最后,仅在平坦区域使用大的搜索窗口以在很大程度上消除噪声。实验结果表明,该方法的视觉质量和评价指标均优于传统的三种方法:增强Lee滤波(ELF),双边滤波(BF)和改进的NL-means。

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