• 中国精品科技期刊
  • CCF推荐A类中文期刊
  • 计算领域高质量科技期刊T1类
Advanced Search
Xu Shaoping, Liu Tingyun, Li Chongxi, Tang Yiling, Hu Lingyan. Noise Level Estimation Algorithm Using Convolutional Neural Network-Based Noise Separation Model[J]. Journal of Computer Research and Development, 2019, 56(5): 1060-1070. DOI: 10.7544/issn1000-1239.2019.20180185
Citation: Xu Shaoping, Liu Tingyun, Li Chongxi, Tang Yiling, Hu Lingyan. Noise Level Estimation Algorithm Using Convolutional Neural Network-Based Noise Separation Model[J]. Journal of Computer Research and Development, 2019, 56(5): 1060-1070. DOI: 10.7544/issn1000-1239.2019.20180185

Noise Level Estimation Algorithm Using Convolutional Neural Network-Based Noise Separation Model

More Information
  • Published Date: April 30, 2019
  • The existing noise level estimation (NLE) algorithms usually adopt the strategy that separates the noise signal from the content of an image to estimate its noise level. Since only a single noisy image can be exploited, these algorithms usually design a variety of complex processes to ensure the accuracy of noise separation, resulting in low execution efficiency. To this end, a novel NLE algorithm using convolutional neural network (CNN)-based noise separation model is proposed in this paper. Specifically, we first add Gaussian noise with different levels to a great amount of representative undistorted images to obtain a training database. Then, we train a CNN-based noise separation model on the training database to obtain the noise mapping from a given noisy image. Considering the fact that the coefficients of the noise mapping show Gaussian distribution behavior, we utilize the generalized Gaussian distribution (GGD) to model the coefficients of the noise mapping, and use two parameters (scale and shape) of the model as the noise level-aware features (NLAF) to describe the level of a noisy image. Finally, an improved back propagation (BP) neural network is used to map the NLAF features to the final noise level. Extensive experiments demonstrate that our method outperforms the most existing classical NLE algorithms in terms of both computational efficiency and estimation accuracy, which makes it more practical to use.
  • Related Articles

    [1]Wang Jiacheng, Wang Kai, Wang Haofen, Du Wen, He Zhidong, Ruan Tong, Liu Jingping. Noise Detection for Distant Supervised Named Entity Recognition[J]. Journal of Computer Research and Development, 2024, 61(4): 916-928. DOI: 10.7544/issn1000-1239.202220999
    [2]Zhang Zhenyu, Jiang Yuan. Label Noise Robust Learning Algorithm in Environments Evolving Features[J]. Journal of Computer Research and Development, 2023, 60(8): 1740-1753. DOI: 10.7544/issn1000-1239.202330238
    [3]Jiang Gaoxia, Wang Wenjian. A Numerical Label Noise Filtering Algorithm for Regression Task[J]. Journal of Computer Research and Development, 2022, 59(8): 1639-1652. DOI: 10.7544/issn1000-1239.20220053
    [4]Xu Shaoping, Liu Tingyun, Luo Jie, Zhang Guizhen, Tang Yiling. An Image Quality-Aware Fast Blind Denoising Algorithm for Mixed Noise[J]. Journal of Computer Research and Development, 2019, 56(11): 2458-2468. DOI: 10.7544/issn1000-1239.2019.20180617
    [5]Xu Shaoping, Zeng Xiaoxia, Tang Yiling, Jiang Shunliang. A Noise Level Estimation Algorithm Using Prior Knowledge of Similar Images[J]. Journal of Computer Research and Development, 2018, 55(12): 2741-2752. DOI: 10.7544/issn1000-1239.2018.20170336
    [6]Ji Zhong, Nie Linhong. Texture Image Classification with Noise-Tolerant Local Binary Pattern[J]. Journal of Computer Research and Development, 2016, 53(5): 1128-1135. DOI: 10.7544/issn1000-1239.2016.20148320
    [7]Qi Ke, Xie Dongqing. Steganalysis of Color Images Based on Noise Model and Channels Integration[J]. Journal of Computer Research and Development, 2013, 50(2): 307-318.
    [8]Wen Qiaonong, Wan Suiren, Xu Shuang. Noise Image Segmentation Using Fisher Criterion and Regularization Level Set Method[J]. Journal of Computer Research and Development, 2012, 49(6): 1339-1347.
    [9]Hong Yi, Wang Zhaoqi, Zhu Dengming, Qiu Xianjie. Generation of Fire Animation Based on Level-Set[J]. Journal of Computer Research and Development, 2010, 47(11): 1849-1856.
    [10]Liu Peng, Zhang Yan, and Mao Zhigang. A Restoration Algorithm for Images Contaminated by Impulse Noise[J]. Journal of Computer Research and Development, 2006, 43(11): 1939-1946.
  • Cited by

    Periodical cited type(3)

    1. 丁坤,刘增泉,张经炜,杨泽南,李喆雨. 基于图像奇异值分解的局部遮挡光伏阵列输出特性建模研究. 综合智慧能源. 2023(02): 53-60 .
    2. 李新. 功率谱估计在舰船噪声特征提取中的应用仿真. 舰船科学技术. 2022(04): 43-46 .
    3. 杨宝军. 基于有限元特征值的船舶螺旋桨噪声数据分类算法. 舰船科学技术. 2021(14): 7-9 .

    Other cited types(3)

Catalog

    Article views (1756) PDF downloads (521) Cited by(6)

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return