IEEE Trans Image Process. 2017 Feb;26(2):1017-1030. doi: 10.1109/TIP.2016.2639447. Epub 2016 Dec 14.
Noise level estimation is crucial in many image processing applications, such as blind image denoising. In this paper, we propose a novel noise level estimation approach for natural images by jointly exploiting the piecewise stationarity and a regular property of the kurtosis in bandpass domains. We design a K-means-based algorithm to adaptively partition an image into a series of non-overlapping regions, each of whose clean versions is assumed to be associated with a constant, but unknown kurtosis throughout scales. The noise level estimation is then cast into a problem to optimally fit this new kurtosis model. In addition, we develop a rectification scheme to further reduce the estimation bias through noise injection mechanism. Extensive experimental results show that our method can reliably estimate the noise level for a variety of noise types, and outperforms some state-of-the-art techniques, especially for non-Gaussian noises.
噪声水平估计在许多图像处理应用中至关重要，例如盲图像去噪。在本文中，我们通过联合利用带通域中的分段平稳性和峰度的正则特性，提出了一种用于自然图像的新型噪声水平估计方法。我们设计了一种基于K均值的算法，将图像自适应地划分为一系列不重叠的区域，假设每个区域的干净版本在所有尺度上都与一个恒定但未知的峰度相关联。然后，将噪声水平估计转化为一个最优拟合这个新峰度模型的问题。此外，我们开发了一种校正方案，通过噪声注入机制进一步降低估计偏差。大量实验结果表明，我们的方法能够可靠地估计各种噪声类型的噪声水平，并且优于一些现有技术，特别是对于非高斯噪声。
