基于各向异性全变分的迭代滤波算法

An Iterative Image Filter Based on Anisotropic Total Variation

空间邻近度和像素值相似度的双边滤波(BF)器在滤波时,由于其值域滤波核系数的计算易受到噪声的干扰,在噪声水平较大时,直接使用噪声图像来指导核函数权值计算的方案不可行。为此,提出一种结合各向异性全变分和BF的图像去噪算法,将各向异性全变分算法与BF算法相结合,首先利用各向异性全变分算法对噪声图像进行处理,得到一幅边缘结构信息较为丰富的结果图像,接着将该结果图像作为BF算法的引导图像来指导值域滤波核系数的计算,为保证算法的稳定性,对上述过程进行迭代处理。此外,为提高各向异性全变分算法的计算效率,引入了Split Bregman迭代算法进行加速处理。实验表明,该算法能在较好去噪的同时,保留较多的边缘结构信息。

Spatial proximity and similarity of the pixel values of bilateral filter in the filter based on the calculation of the range of filter kernel coefficient is susceptible to noise interference.When the noise level is high,the direct use of noise image to guide the kernel weight computation program is not feasible.Therefore,in this paper,the anisotropic total variation and bilateral filtering are combined.Firstly,the image is processed by the anisotropic total variation model,and the result image with rich edge structure information is obtained.Then the calculation results of image as a guide bilateral filtering image to guide the range of filter kernel coefficient.In order to ensure the stability of the algorithm,the above process is iterated.In addition,in order to improve the computational efficiency of the anisotropic total variation model,the Split Bregman iterative algorithm is introduced to accelerate the computation.The experimental results show that the proposed algorithm can preserve more edge information while denoising.