A Robust Noise Estimation Algorithm Based on Redundant Prediction and Local Statistics

被引:0
|
作者
Xie, Huangxin [1 ]
Yi, Shengxian [2 ]
Yang, Zhongjiong [1 ]
机构
[1] Cent South Univ, Sch Mech & Elect Engn, State Key Lab High Performance Complex Mfg, Changsha 410083, Peoples R China
[2] Changsha Univ, Sch Mech & Elect Engn, Changsha 410022, Peoples R China
关键词
homogenous patches; median absolute deviation; noise estimation; nonlinear regression; redundant dimensions; LEVEL ESTIMATION; PARAMETER-ESTIMATION; GAUSSIAN-NOISE; SIGNAL; MODEL; DCT;
D O I
10.3390/s24010168
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Blind noise level estimation is a key issue in image processing applications that helps improve the visualization and perceptual quality of images. In this paper, we propose an improved block-based noise level estimation algorithm. The proposed algorithm first extracts homogenous patches from a single noisy image using local features, obtaining the covariance matrix eigenvalues of the patches, and constructs dynamic thresholds for outlier discrimination. By analyzing the correlations between scene complexity, noise strength, and other parameters, a nonlinear discriminant coefficient regression model is fitted to accurately predict the number of redundant dimensions and calculate the actual noise level according to the statistical properties of the elements in the redundancy dimension. The experimental results show that the accuracy and robustness of the proposed algorithm are better than those of the existing noise estimation algorithms in various scenes under different noise levels. It performs well overall in terms of performance and execution speed.
引用
收藏
页数:17
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