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

Authors :
Huangxin Xie
Shengxian Yi
Zhongjiong Yang
Source :
Sensors, Vol 24, Iss 1, p 168 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

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.

Details

Language :
English
ISSN :
14248220
Volume :
24
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Sensors
Publication Type :
Academic Journal
Accession number :
edsdoj.947ea9e878d141a4bfa016e7153f5e02
Document Type :
article
Full Text :
https://doi.org/10.3390/s24010168