各向异性全变分引导滤波及其Split Bregman方法

Anisotropic Total Variation Guided Filtering and Its Split Bregman Algorithm

引导滤波(GF)去噪的关键是选取一幅包含清晰结构信息的引导图像。为提高GF的去噪效果,提出一种由各向异性全变分(ATV)引导的滤波方法。首先利用ATV模型对噪声图像进行光滑处理,生成包含良好结构信息的引导图像,然后利用GF进行处理。为提高算法的稳健性,对上述过程进行迭代处理。由于计算全变分模型的传统迭代方法速度较慢,因此采用Split Bregman迭代方法进行加速处理。实验结果表明:该算法不仅在峰值信噪比、归一化均方误差和结构相似性等客观指标上具有优势,而且计算速度比传统迭代方法提高了约30倍。该算法可以较好地快速去除噪声,并能较好地保持图像中的结构和边缘特征等细节信息。

An ideal guided image with good structure is the key to guided filtering （GF） denoising. In order to improve the denoising effect of the GF, an anisotropic total variation （ATV） based on the GF is proposed. First, the noisy image is smoothed by the ATV model to obtain a good structure information image. Then the image is served as the guided image in the GF algorithm. And an iterative processing is used for improving the algorithm robustness. Considering that the traditional iterative method for the total variation model is time consuming, the Split Bregman iterative method is introduced to speed up the whole process. The experimental results indicate that the proposed method not only has certain advantages in peak signal noise ratio, normalized mean square error and structural similarity, but also increases the computation speed by nearly 30 times compared with the related traditional iterative method. It can effectively remove the noises while preserving more structure and edge details.