基于非局部分类处理的SAR图像降斑

针对合成孔径雷达(synthetic aperture radar, SAR)图像不同内容间的统计特性差距较大以及采用非局部降斑方法处理SAR图像时会产生图像细节丢失及伪影等现象,提出了一种基于非局部分类处理的SAR图像降斑方法。首先对相似块方差系数的均值采用阈值比较的方法将图像分为同质区和异质区,在寻找目标块对应的相似块时,根据图像块中心像素点所属的类别选择合适的参数进行块匹配,并在获得相似块后分别使用加权平均的方法处理同质区目标块和3D变换域硬阈值收缩方法处理异质区目标块。该方法不仅能在块匹配过程中选择不同的块尺寸和搜索范围以提高块匹配的精度,而且使用不同方法处理同质区和异质区时能够在图像降斑与细节保持的权衡中达到更好的平衡。实验结果表明,该方法充分利用同质区和异质区的特点,结合不同的方法进行分类处理可有效提高图像降斑性能,在SAR图像降斑性能指标和视觉效果方面均达到了较高水平。

SAR image despeckling via the classification-based non-local clustering

Because different regions of the synthetic aperture radar (SAR) image have different statistical characteristics, the phenomenon of missing details and “ghost” artifacts often occur in the traditional non local SAR image despeckling method. As a result, a classification based SAR image despeckling method is proposed. Firstly, the image is classified into homogeneous regions and heterogeneous regions through comparing the variation of average coefficient with the threshold value. Secondly, according to the classification of the central pixel of the target block, the appropriate parameters are selected to perform block matching. Finally, the target block located in homogeneous regions is proceed by weighted average with similar blocks of the target block. On the other hand, the target block located in heterogeneous regions is performed by 3D hard thresholding shrinkage with similar blocks of the target block. The proposed method can not only improve the accuracy of block matching through adjusting the block size and the search window size on the basis of the characteristics of image blocks, but also achieve a satisfied balance between smoothing (in homogeneous region) and feature preservation (in heterogeneous region) by selecting different methods to deal with different regions. The experiment results show that the proposed method makes full use of the characteristics of homogeneous and heterogeneous regions, which combine the advantage of different methods to improve the despeckling performance and reach a high level in terms of both performance index and visual quality.