基于增强高阶非凸全变分模型的图像去噪算法

为了在缓解阶梯效应的同时更好地保留去噪后图像的细节信息,提出一种基于增强高阶非凸全变分(higher order non-convex total variation, HONTV)模型的图像去噪算法。该算法将每一次去噪后的图像和原始图像取平均作为增强HONTV模型下一次循环的输入并更新参数,然后采用增广拉格朗日乘子法和交替方向乘子法进行循环求解,经过多次迭代,最终得到的去噪图像包含较多的细节信息。在基于全变分的图像去噪方法中,对添加不同标准差大小的高斯白噪声的测试图像和视频进行实验。实验结果表明,所提算法在视觉性能和客观评价指标方面均优于对比算法。

Image denoising algorithm based on boosting high order non-convex total variation model

In order to ease the stair casing artifacts and better preserve the details of the image after denoising, this paper proposes an image denoising algorithm based on the boosting high order non-convex total variation (HONTV) model. By averaging each denoised image and the original image as the input of the next cycle of the boosting HONTV model and updating the parameters, the augmented Lagrangian method and the alternating direction method of multipliers are used to solve the model. After multiple iterations, the resulting denoised image contains more detail information. In the image denoising method based on total variation, the experimental results show that the proposed algorithm outperforms the comparison algorithm in terms of visual performance and objective evaluation index for adding Gaussian white noise test images and videos with different standard deviations.