SAR image despeckling based on edge detection and nonsubsampled second generation bandelets
SAR image despeckling based on edge detection and nonsubsampled second generation bandelets
复制标题
基于边缘检测和非下采样第二代Bandelet的SAR图像去斑
DOI：
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发表时间：
2009-06
期刊：
影响因子：
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通讯作者：
Jiao Licheng
中科院分区：
文献类型：
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作者：
Liu Fang;Gao Xi;Zhang Wenge;Jiao Licheng
为了有效地保留合成孔径雷达（SAR）图像的清晰特征和细节，提出了一种非下采样第二代Bandelet变换（NSBT）域边缘检测的SAR图像斑点抑制算法。首先，利用Canny算子检测和去除SAR图像的边缘。然后利用对边缘具有最佳逼近能力的NSBT和硬阈值准则对边缘去除后的图像进行细节逼近，同时对边缘去除后的图像进行斑点噪声抑制。最后，将去除的边缘添加到重建图像中。由于对边缘进行了检测和保护，并使用了NSBT，该算法达到了同时实现去斑点和保持边缘及细节的最佳效果。实验结果表明，该算法的主观视觉效果和主要客观性能指标均优于贝叶斯小波收缩边缘检测算法和贝叶斯最小二乘-高斯尺度混合算法（BLS-GSM）。
To preserve the sharp features and details of the synthetic aperture radar (SAR) image effectively when despeckling, a despeckling algorithm with edge detection in nonsubsampled second generation bandelet transform (NSBT) domain is proposed. First, the Canny operator is utilized to detect and remove edges from the SAR image. Then the NSBT which has an optimal approximation to the edges of images and a hard thresholding rule are used to approximate the details while despeckling the edge-removed image. Finally, the removed edges are added to the reconstructed image. As the edges are detected and protected, and the NSBT is used, the proposed algorithm reaches the state-of-the-art effect which realizes both despeckling and preserving edges and details simultaneously. Experimental results show that both the subjective visual effect and the mainly objective performance indexes of the proposed algorithm outperform that of both Bayesian wavelet shrinkage with edge detection and Bayesian least square-Gaussian scale mixture (BLS-GSM).