Fast Image Noise Level Estimation Algorithm Based on Principal Component Analysis and Deep Neural Network

XU Shao-ping; LI Chong-xi; LIN Guan-xi; TANG Yi-ling; HU Ling-yan

doi:10.3969/j.issn.0372-2112.2019.02.003

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Fast Image Noise Level Estimation Algorithm Based on Principal Component Analysis and Deep Neural Network

更新时间：2025-07-16

- Fast Image Noise Level Estimation Algorithm Based on Principal Component Analysis and Deep Neural Network
- Acta Electronica Sinica Vol. 47, Issue 2, Pages: 274-281(2019)
- 作者机构：
  
  南昌大学信息工程学院,江西,南昌,330031
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2019.02.003
  CLC： TP391
- Published Online：25 February 2019，
  
  Published：2019
- 稿件说明：
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XU Shao-ping, LI Chong-xi, LIN Guan-xi, et al. Fast Image Noise Level Estimation Algorithm Based on Principal Component Analysis and Deep Neural Network[J]. Acta Electronica Sinica, 2019, 47(2): 274-281.
DOI：

XU Shao-ping, LI Chong-xi, LIN Guan-xi, et al. Fast Image Noise Level Estimation Algorithm Based on Principal Component Analysis and Deep Neural Network[J]. Acta Electronica Sinica, 2019, 47(2): 274-281. DOI： 10.3969/j.issn.0372-2112.2019.02.003.

摘要

鉴于从噪声图像分解获得的原生图块集合的协方差矩阵前若干个特征值（按照升序排序）与图像噪声水平值具有强相关性，提出了一种基于主成分分析和深度神经网络的快速噪声水平估计算法.该算法首先选用原生图块集合协方差矩阵前若干个特征值构成刻画图像噪声水平高低的特征矢量，然后在大量有代表性且已标定噪声水平值的噪声图像集合上利用深度神经网络训练预测模型以实现将特征矢量直接映射为噪声水平值，最后为获得更高的预测准确性，采用粗精预测模型相结合的两步预测方式实现.实验表明：文中算法在各个噪声级别上都具有稳定的预测准确性，且执行效率非常高，作为降噪算法的前置预处理模块具有更好的综合优势.

Abstract

Considering the fact that there exists the strong correlation between the first several eigenvalues (in ascending order)of the covariance matrix of the raw patches extracted from a noisy image and its noise level

we proposed a novel fast multiple image-based noise level estimation (FMNLE)algorithm using the principal component analysis (PCA)and the deep neural network (DNN).Specifically

we selected the first several eigenvalues of the raw patches to form a feature vector characterizing the noise level of an image.Then

we employed deep neural network to train an estimation model on a large number of representative natural images corrupted with known noise levels

by which the feature vector can be directly mapped into the corresponding noise level.To obtain higher estimation accuracy

a two-step estimation strategy was adopted.Extensive experiments show that

the estimation accuracy of the proposed algorithm is stable at each noise level with good efficiency

demonstrating a better comprehensive advantage as the pre-processing module for denoising algorithms.

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