基于空间自适应变分的条带噪声去除方法

Destriping method based on spatial adaptive total variation

遥感图像经常被条带噪声污染,导致图像质量下降。为了去除条带噪声,本文提出了一种基于空间自适应变分的噪声抑制方法。首先,对条带噪声建模,并利用信噪比较高的区域估计出模型的增益和偏置;然后,在变分法的框架下,构建能量函数,并引入空间自适应正则因子,根据图像空间信息自适应调整正则参数;最后,采用分裂式Bregman算法优化能量函数,得到去噪图像的最优解。实验表明,本文算法可以将实际遥感图像等效视数由37.26提高到76.48,辐射质量提升因子提高到8.52 d B。本文算法能够有效去除条带噪声,保留图像细节,改善图像质量。

Remote sensing images often suffer from stripes,which degrades the image quality.To remove stripes,a new destriping method based on spatial adaptive total variation is proposed.Firstly,the stripe model is built and the gain and bias of the model are estimated with high SNR regions.Then,an energy function is designed under the framework of total variation and an adaptive regulation parameter is introduced,which adapts to the spatial varying information.At last,the split Bregman approach is exploited to optimize the minimization problem.Experiment results demonstrate that the proposed method can improve the equivalent number of looks（ ENL） from 37.26 to 76.48,and the improvement factor（ IF） of radiometric quality is improved to 8.52 d B.The proposed method can effectively remove stripes and preserve detail information.