基于Pareto先验的混合噪声去除
Mixed Noise Removal with Pareto Prior
摘要：
加性高斯白噪声（AWGN）和脉冲噪声（IN）混合污染的图像去噪是一个基本而具有挑战性的问题。脉冲干扰的存在不可避免地影响噪声的分布，从而大大降低了传统AWGN去噪器的性能。现有的方法通过引入加权矩阵来补偿IN的影响，但是，该加权矩阵缺乏适当的先验知识，因此难以准确估计。为了解决这个问题，我们利用帕累托分布作为先验的加权矩阵，基于此提出了一个准确和强大的权重估计混合噪声去除。特别地，假设像素的相对小的部分被IN污染，其应当具有具有小值的权重并且然后被惩罚掉。这种现象可以用帕累托分布的类型1来描述。因此，武装的Pareto分布，我们制定的问题，混合噪声去除贝叶斯框架，其中非局部自相似先验进一步利用采用非局部低秩近似。与现有方法相比，该方法能自适应地估计权矩阵，且对不同噪声水平具有较好的鲁棒性，从而提高了去噪性能。广泛使用的图像数据集上的实验结果表明，我们提出的方法的优越性，以国家的艺术。
英文摘要：
Denoising images contaminated by the mixture of additive white Gaussian noise (AWGN) and impulse noise (IN) is an essential but challenging problem. The presence of impulsive disturbances inevitably affects the distribution of noises and thus largely degrades the performance of traditional AWGN denoisers. Existing methods target to compensate the effects of IN by introducing a weighting matrix, which, however, is lack of proper priori and thus hard to be accurately estimated. To address this problem, we exploit the Pareto distribution as the priori of the weighting matrix, based on which an accurate and robust weight estimator is proposed for mixed noise removal. Particularly, a relatively small portion of pixels are assumed to be contaminated with IN, which should have weights with small values and then be penalized out. This phenomenon can be properly described by the Pareto distribution of type 1. Therefore, armed with the Pareto distribution, we formulate the problem of mixed noise removal in the Bayesian framework, where nonlocal self-similarity priori is further exploited by adopting nonlocal low rank approximation. Compared to existing methods, the proposed method can estimate the weighting matrix adaptively, accurately, and robust for different level of noises, thus can boost the denoising performance. Experimental results on widely used image datasets demonstrate the superiority of our proposed method to the state-of-the-arts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
