Image Mixoeed Noise Removal Algorithm Based on Convolutional Neural Network

LEI Jun-feng; WANG He; LIU En-yu; XIAO Jin-sheng; XIE Wen-juan

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Image Mixoeed Noise Removal Algorithm Based on Convolutional Neural Network

更新时间：2026-09-04

- Image Mixoeed Noise Removal Algorithm Based on Convolutional Neural Network
- Vol. 34, Issue 12, Pages: 11-15(2017)
- 作者机构：
  
  1. 武汉大学电子信息学院,湖北,武汉,430079
  2. 烽火通信科技股份有限公司,湖北,武汉,430074
- 作者简介：
- 基金信息：
- DOI：
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- Published：2017
- 稿件说明：
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雷俊锋, 王赫, 刘恩雨, 肖进胜, 谢文娟. 基于卷积神经网络的图像混合噪声去除算法[J]. 微电子学与计算机, 2017, 34(12): 11-15.

LEI Jun-feng, WANG He, LIU En-yu, et al. Image Mixoeed Noise Removal Algorithm Based on Convolutional Neural Network[J]. 2017, 34(12): 11-15.
雷俊锋, 王赫, 刘恩雨, 肖进胜, 谢文娟. 基于卷积神经网络的图像混合噪声去除算法[J]. 微电子学与计算机, 2017, 34(12): 11-15. DOI：

LEI Jun-feng, WANG He, LIU En-yu, et al. Image Mixoeed Noise Removal Algorithm Based on Convolutional Neural Network[J]. 2017, 34(12): 11-15. DOI：

摘要

针对现有的去噪算法

只能去除某一或两种特定的噪声

而对其他类型的噪声无法去除的缺陷

结合现有一些优秀的网络模型并改进

提出了基于卷积神经网络的图像混合噪声的去除算法.采用9层卷积网络

分别经过特征提取、维度收缩、非线性映射、维度放大和图像重构对含噪图像进行训练最终得到去噪模型.实验结果表明

算法生成的网络模型适用于含不同类型、不同程度的含噪图像的去噪

且在主观视觉效果和客观指标上均有很好的结果.

Abstract

Aiming at the drawbacks of the denoised algrithms

that can only removal one or two specifickind of noise and are invalid for others

we combine some excellent neural network model and proposed the image mixed noise removal algorithm based on convolutional neural network. 9 convolution layers are adopted

and noise images are trained through feature extraction

shrinking

non-liner mapping

expanding and reconstruction. Experimental results show that the algorithm achieves better denoised results and is suitable for different kinds of mixed noise images. The subjective visual effect and objective evaluation indices are improved obviously.

关键词

Keywords

references

Jiang J, Zhang L, Yang J. Mixed noise removal by weighted encoding with sparse nonlocal regularization[J]. IEEE transactions on image processing, 2014, 23(6): 2651-2662. DOI: 2651

Aggarwal H K, Majumdar A. Hyperspectral image denoising using spatio-spectral total variation[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(3): 442-446. DOI: 442

Knaus C, Zwicker M. Dual-domain image denoising[C]//Image Processing (ICIP), 2013 20th IEEE International Conference on. Melbourne, Australia, IEEE, 2013: 440-444.

Liu J, Tai X C, Huang H, et al. A weighted dictionary learning model for denoising images corrupted by mixed noise[J]. IEEE transactions on image processing, 2013, 22(3): 1108-1120. DOI: 1108

Cai J F, Chan R H, Nikolova M. Fast two-phase image deblurring under impulse noise[J]. Journal of Mathematical Imaging and Vision, 2010, 36(1): 46-53. DOI: 46

Dong C, Loy C C, Tang X. Accelerating the super-resolution convolutional neural network[C]//European Conference on Computer Vision. China, Hong Kong, Springer International Publishing, 2016: 391-407.

Hwang H, Haddad R A. Adaptive median filters: new algorithms and results[J]. IEEE Transactions on image processing, 1995, 4(4): 499-502. DOI: 499

Mitra K, Veeraraghavan A, Chellappa R. Robust rvm regression using sparse outlier model[C]//Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on. San Francisco, CA, USA, IEEE, 2010: 1887-1894.

武英, 吴海勇. 一种自适应图像去噪混合滤波方法[J]. 计算机工程与应用, 2010, 46(7): 168-170. DOI: 168

肖进胜, 李文昊, 姜红, 等. 基于双域滤波的三维块匹配视频去噪算法[J]. 通信学报, 2015, 35(9): 91-97. DOI: 91

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Related Institution

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