ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2018, Vol. 55 ›› Issue (12): 2741-2752.doi: 10.7544/issn1000-1239.2018.20170336

• 图形图像 • 上一篇    下一篇

基于多图像先验知识的噪声水平评估算法

徐少平,曾小霞,唐祎玲,江顺亮   

  1. (南昌大学信息工程学院 南昌 330031) (xushaoping@ncu.edu.cn)
  • 出版日期: 2018-12-01
  • 基金资助: 
    国家自然科学基金项目(61662044,61163023,51765042,81501560);江西省自然科学基金项目(20171BAB202017)

A Noise Level Estimation Algorithm Using Prior Knowledge of Similar Images

Xu Shaoping, Zeng Xiaoxia, Tang Yiling, Jiang Shunliang   

  1. (School of Information Engineering, Nanchang University, Nanchang 330031)
  • Online: 2018-12-01

摘要: 为解决基于单图像噪声水平评估算法抗干扰能力低和执行效率不高的问题,提出一种基于多图像先验知识的噪声水平评估算法.首先,在具有广泛代表性且未受噪声干扰图像集合上添加已知噪声水平的高斯噪声构建失真样本图像集合,并提取每幅样本图像中的若干统计特征值构成描述他们噪声水平值高低的噪声水平感知特征矢量.然后,利用样本图像上所提取的特征矢量及对其所施加的噪声水平值构成样本库.在评估时,先提取待评价噪声图像的特征矢量并在样本库中检索出与之类似的若干特征矢量及它们所对应的噪声水平值, 之后基于这些样本信息以加权均值法估算待评价图像的噪声水平值.实验数据表明:较现有的噪声水平评估算法,新算法不仅在高、中、低噪声水平下都具有稳定的预测准确度,而且评估速度快.尤其是对于高斯噪声中伴有脉冲或者泊松噪声情况,具有较好的抗干扰能力.

关键词: 图像降噪, 噪声水平估计, 噪声水平感知特征, 加权均值估计, 执行效率, 鲁棒性

Abstract: To resolve the problem that the existing single image-based noise level estimation (SNLE) algorithms are of poor anti-interference ability and low execution efficiency, a multi-image based noise level estimation (MNLE) algorithm using prior knowledge of similar images is proposed. Specifically, a set of distorted images is first constructed by adding different pre-defined noise levels to some representative noise-free images, and several natural statistical values extracted from each distorted image are used to form the noise level-aware feature vector. Then, the feature vector extracted from each distorted image and its corresponding noise level are used to construct a sample database. During the noise level estimation stage, the feature vector of an image to be estimated is extracted by the same method as the preparation stage, and several feature vectors similar to the extracted feature vector and their corresponding noise levels are chosen from the sample database. As such, the noise level of the image to be estimated is estimated with a weighted average approach. Extensive experimental results show that the proposed MNLE algorithm not only is of high efficiency but also has stable prediction accuracy at high, medium, and low noise levels. For the Gaussian noise mixed with impulse noise or Poisson noise, it also has good anti-interference ability.

Key words: image denoising, noise level estimation (NLE), noise level-aware feature, weighted average estimation, execution efficiency, robustness

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