| 摘 要: 为改善非局部均值(Non-Local Means，NLM)算法的去噪性能，解决NLM算法参数分配以及去噪后图像边缘模糊等问题，对基于区域划分的非局部均值图像去噪算法进行了改进。通过Canny边缘检测算子和形态学膨胀处理对图像进行区域划分，对划分后的不同区域进行参数的调整，并对欧氏距离和权重函数进行改进，提升NLM算法的去噪性能，使去噪后的图像保留更多的细节纹理信息。实验结果表明，该算法相比于传统的NLM去噪算法、参数自适应的NLM算法以及基于转动惯量的改进权重函数的NLM算法，有着更好的峰值信噪比和结构相似度值。 |
| 关键词: 图像去噪；非局部均值；欧氏距离；权重函数 |
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中图分类号: TP391
文献标识码: A
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| 基金项目: 国家重点研发计划项目(2019YFB1310000). |
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| Improvement of Non-Local Means Image Denoising Algorithm based on Region Division |
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BAI Lie, CAI Yun, JIANG Lin
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(Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Wuhan 430081, China )
2425191125@qq.com; 1caiyun@163.com; jlxyhjl@163.com
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| Abstract: In order to improve the denoising performance of the Non-Local Means (NLM) algorithm and solve the problem of parameter allocation and image edge blur after denoising, this paper proposes to improve the NLM image denoising algorithm based on region division. By Canny edge detection operator and morphological processing of dilation, image is divided into different regions where parameters are adjusted, and Euclidean distance and weight functions are improved, so that the denoising performance of the NLM algorithm is enhanced and the denoised image retains more detailed texture information. The experimental results show that the improved algorithm has better peak signal-to-noise ratio and structural similarity values, compared to traditional NLM denoising algorithms, parameter adaptive NLM algorithms, and improved weight function NLM algorithms based on rotational inertia. |
| Keywords: image denoising; non-local means; Euclidean distance; weight function |
