边缘保持图像平滑是很多计算机视觉和图形学算法的关键步骤,基于l1范数的边缘保持图像平滑算法是现有算法中性能较好的一种,但是该算法的平滑结果中仍然存在很多未被平滑的纹理。为了改进算法的平滑结果,提出了一种新的基于重加权l1范数的边缘保持图像平滑算法,该算法将原始的基于l1范数的图像平滑算法与重加权l1范数最小化相结合,通过使用重加权方法使得解更加稀疏；并通过实验对该算法的性能进行了验证。结果表明：和基于l1范数的图像平滑算法以及一些最新的图像平滑算法相比,文中提出的算法可以更加有效地平滑图像,平滑结果中仅有很少的纹理残留；采用重加权l1范数可以改进现有的基于l1范数的图像平滑算法的图像平滑结果。

Edge-preserving image smoothing is a key preprocessing step of many computer vision and graphics algorithms. Edge-preserving image smoothing algorithm based on l1 norm outperforms many existing image smoothing algorithms. However, there are still many remaining textures in the smoothed results. In order to improve the smoothing effect of the algorithm, a new image smoothing algorithm based on reweighted l1 norm was proposed. The proposed algorithm combines the original l1 norm-based image smoothing algorithm and reweighed l1 norm minimization. The solution was made sparser through the reweighting method. The performance of the algorithm was evaluated through experiments. Experimental results show that, compared to the original l1 norm-based image smoothing algorithm and several other existing image smoothing algorithms, the proposed algorithm can smooth images very effectively and there is little texture remained in the smoothed results. Using reweighted l1 norm can improve the smoothing effect of the original l1 norm-based image smoothing algorithm.