基于改进非局部均值的红外图像混合噪声去除方法

李方舟; 赵耀宏; 向伟; 刘海峥

doi:10.3788/IRLA201948.S128001

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基于改进非局部均值的红外图像混合噪声去除方法

李方舟, 赵耀宏, 向伟, 刘海峥

文章导航 > 红外与激光工程 > 2019 > 48(S1): 163-173

李方舟, 赵耀宏, 向伟, 刘海峥. 基于改进非局部均值的红外图像混合噪声去除方法[J]. 红外与激光工程, 2019, 48(S1): 163-173. doi: 10.3788/IRLA201948.S128001

引用本文:

李方舟, 赵耀宏, 向伟, 刘海峥. 基于改进非局部均值的红外图像混合噪声去除方法[J]. 红外与激光工程, 2019, 48(S1): 163-173. doi: 10.3788/IRLA201948.S128001

Li Fangzhou, Zhao Yaohong, Xiang Wei, Liu Haizheng. Infrared image mixed noise removal method based on improved NL-means[J]. Infrared and Laser Engineering, 2019, 48(S1): 163-173. doi: 10.3788/IRLA201948.S128001

Citation:

Li Fangzhou, Zhao Yaohong, Xiang Wei, Liu Haizheng. Infrared image mixed noise removal method based on improved NL-means[J]. Infrared and Laser Engineering, 2019, 48(S1): 163-173. doi: 10.3788/IRLA201948.S128001

基于改进非局部均值的红外图像混合噪声去除方法

doi: 10.3788/IRLA201948.S128001

李方舟^1,2,3,4,5,
赵耀宏^1,2,4,5,
向伟^1,2,4,5,
刘海峥^1,2,4,5

1.
中国科学院沈阳自动化研究所,辽宁沈阳 110016;
2.
中国科学院机器人和智能制造创新研究院,辽宁沈阳 110016;
3.
中国科学院大学,北京 100049;
4.
中国科学院光电信息处理重点实验室,辽宁沈阳 110016;
5.
辽宁省图像理解与计算机视觉重点实验室,辽宁沈阳 110016

基金项目:

装备预研领域基金（61404150203）

详细信息

作者简介:
李方舟(1993-),男,硕士生,主要从事红外图像处理等方面的研究。Email:lifangzhou@sia.cn

中图分类号: TP391

Infrared image mixed noise removal method based on improved NL-means

Li Fangzhou^1,2,3,4,5,
Zhao Yaohong^1,2,4,5,
Xiang Wei^1,2,4,5,
Liu Haizheng^1,2,4,5

1.
Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang 110016,China;
2.
Institutes for Robotics and Intelligent Manufacturing,Chinese Academy of Sciences,Shenyang 110016,China;
3.
University of Chinese Academy of Sciences,Beijing 100049,China;
4.
Key Laboratory of Opto-electronic Information Processing,Chinese Academy of Sciences,Shenyang 110016,China;
5.
The Key Laboratory of Image Understanding and Computer Vision,Shenyang 110016,China

摘要: 传统的去噪算法无法有效去除红外图像中的条纹与随机混合噪声。针对这一问题，提出了一种改进的基于非局部均值（NL-means）的混合噪声去除方法。首先，分析了非局部均值算法处理混合噪声的问题，并用一组实验分析了红外图像块中混合噪声的特性。根据实验结果，文中用有色高斯模型对混合噪声进行建模，并基于Mahalanobis距离改进了传统的基于欧氏距离的块相似性度量方法，使之对图像中不同复杂程度的区域进行自适应。仿真和真实数据实验均表明：文中算法相比于传统的图像去噪算法，能较好地去除条纹与随机混合噪声。
- 图像去噪 /
- 单帧去条纹 /
- 红外图像 /
- 非局部均值
Abstract: Typical denoising algorithm were unable to effectively remove the mixed noise of stripe and random noise in infrared images. To solve this problem, an improved NL-means filter was proposed. Firstly, the problem in NL-means algorithm dealing with mixed noise was analyzed, and an experiment was performed to analyze the characteristic of the mixed noise in image patches of infrared images. Based on the experiment results, the mixed noise was modelled using colored Gaussian model, and the ordinary patch similarity index was improved using Mahalanobis distance instead of Euclidean distance, so that it adapts to the local with different complexity in the image. Both simulated and real data experiments show that the algorithm can effectively remove the stripe and random mixed noise compared with traditional image denoising algorithms.
- image denoising /
- single-frame-based destriping /
- infrared image /
- NL-means

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基于改进非局部均值的红外图像混合噪声去除方法

doi: 10.3788/IRLA201948.S128001

1. 中国科学院沈阳自动化研究所,辽宁沈阳 110016;

2. 中国科学院机器人和智能制造创新研究院,辽宁沈阳 110016;

3. 中国科学院大学,北京 100049;

4. 中国科学院光电信息处理重点实验室,辽宁沈阳 110016;

5. 辽宁省图像理解与计算机视觉重点实验室,辽宁沈阳 110016

作者简介:
李方舟(1993-),男,硕士生,主要从事红外图像处理等方面的研究。Email:lifangzhou@sia.cn

收稿日期: 2018-11-01
修回日期: 2018-12-14
刊出日期: 2019-04-25

基金项目:

装备预研领域基金（61404150203）

中图分类号: TP391

关键词:

摘要: 传统的去噪算法无法有效去除红外图像中的条纹与随机混合噪声。针对这一问题，提出了一种改进的基于非局部均值（NL-means）的混合噪声去除方法。首先，分析了非局部均值算法处理混合噪声的问题，并用一组实验分析了红外图像块中混合噪声的特性。根据实验结果，文中用有色高斯模型对混合噪声进行建模，并基于Mahalanobis距离改进了传统的基于欧氏距离的块相似性度量方法，使之对图像中不同复杂程度的区域进行自适应。仿真和真实数据实验均表明：文中算法相比于传统的图像去噪算法，能较好地去除条纹与随机混合噪声。

English Abstract

Infrared image mixed noise removal method based on improved NL-means

1. Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang 110016,China;

2. Institutes for Robotics and Intelligent Manufacturing,Chinese Academy of Sciences,Shenyang 110016,China;

3. University of Chinese Academy of Sciences,Beijing 100049,China;

4. Key Laboratory of Opto-electronic Information Processing,Chinese Academy of Sciences,Shenyang 110016,China;

5. The Key Laboratory of Image Understanding and Computer Vision,Shenyang 110016,China

Received Date: 2018-11-01
Rev Recd Date: 2018-12-14
Publish Date: 2019-04-25

Keywords:

Abstract: Typical denoising algorithm were unable to effectively remove the mixed noise of stripe and random noise in infrared images. To solve this problem, an improved NL-means filter was proposed. Firstly, the problem in NL-means algorithm dealing with mixed noise was analyzed, and an experiment was performed to analyze the characteristic of the mixed noise in image patches of infrared images. Based on the experiment results, the mixed noise was modelled using colored Gaussian model, and the ordinary patch similarity index was improved using Mahalanobis distance instead of Euclidean distance, so that it adapts to the local with different complexity in the image. Both simulated and real data experiments show that the algorithm can effectively remove the stripe and random mixed noise compared with traditional image denoising algorithms.