Fast noise level estimation algorithm based on principal component analysis transform and nonlinear rectification
Fast noise level estimation algorithm based on principal component analysis transform and nonlinear rectification
复制标题
DOI：
10.1117/1.jei.27.1.010501
复制
发表时间：
2018-02
影响因子：
1.1
通讯作者：
Shaoping Xu;Xiaoxia Zeng;Yinnan Jiang;Yiling Tang
中科院分区：
文献类型：
--
作者：
Shaoping Xu;Xiaoxia Zeng;Yinnan Jiang;Yiling Tang
提出了一种基于非迭代主成分分析（PCA）的噪声水平估计（NLE）算法，该算法解决了用两步方案估计噪声水平的问题。首先，我们从一个给定的噪声图像随机提取一些原始补丁，并采取的原始补丁的协方差矩阵的最小特征值作为噪声水平的初步估计。接下来，使用非线性映射（校正）函数直接获得最终估计，该函数在被不同已知噪声水平损坏的一些代表性噪声图像上进行训练。实验结果表明，与现有的非线性估计算法相比，该算法能够可靠地估计出图像中的噪声水平，并且在较宽的图像内容和噪声水平范围内具有较好的鲁棒性，在速度和精度之间取得了较好的平衡。
We proposed a noniterative principal component analysis (PCA)-based noise level estimation (NLE) algorithm that addresses the problem of estimating the noise level with a two-step scheme. First, we randomly extracted a number of raw patches from a given noisy image and took the smallest eigenvalue of the covariance matrix of the raw patches as the preliminary estimation of the noise level. Next, the final estimation was directly obtained with a nonlinear mapping (rectification) function that was trained on some representative noisy images corrupted with different known noise levels. Compared with the state-of-art NLE algorithms, the experiment results show that the proposed NLE algorithm can reliably infer the noise level and has robust performance over a wide range of image contents and noise levels, showing a good compromise between speed and accuracy in general.