马凤颖,,
张长森
河南理工大学物理与电子信息学院 焦作 454000
基金项目:国家自然科学基金(52074305)，河南省科技攻关(212102210005)，河南理工大学光电传感与智能测控河南省工程实验室开放基金(HELPSIMC-2020-00X)
详细信息
作者简介:王满利：男，1981年生，博士，研究方向为信息与通信工程
马凤颖：女，1994年生，硕士生，研究方向为现代通信技术
张长森：男，1969年生，教授，研究方向为现代通信技术
通讯作者:马凤颖 15139168603@163.com
中图分类号:TN911.73; TP391.41计量
文章访问数:237
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被引次数:0
出版历程
收稿日期:2020-12-30
修回日期:2021-05-22
网络出版日期:2021-06-07
刊出日期:2021-11-23
Mixed Noise Suppression Algorithm Based on Developable Local Surface of Image
Manli WANG,Fengying MA,,
Changsen ZHANG
School of Physics &Electronic Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
Funds:The National Natural Science Foundation of China (52074305), The Science and Technology Research in Henan Province (212102210005), The Henan Polytechnic University Photoelectric Sensing and Intelligent Measurement and Control Provincial Program Laboratory Open Fund (HELPSIMC-2020-00X)
摘要
摘要:为满足基于旋翼无人机(UAV)载具的室外目标检测所需的低资源开销混合噪声抑制，该文提出一种基于图像局部曲面可展化的混合噪声抑制算法(DLS)，该算法实现了局部曲面可展化算法和分层降噪算法优势互补，达到了两算法各自无法企及的降噪效果。首先，对图像进行局部可展化处理，抑制图像的椒盐噪声和低密度高斯噪声，得到初步降噪图像；接着，在空间域和傅里叶域分层降噪，在去除高斯噪声残余的同时，最大限度地保留图像边缘、纹理等细节；最后，迭代局部曲面可展化和分层降噪，进一步去除混合噪声残余成分，达到抑制目标检测图像混合噪声的目的。实验结果表明，在去除图像混合噪声时，相比于其他7种降噪算法，本文算法具有一定的优势，其降噪图像的主观视觉指标和客观数据指标统计优于其他7种算法。
关键词:图像降噪/
混合噪声/
局部曲面可展化/
分层降噪/
迭代降噪
Abstract:In order to meet the requirement of low resource cost and mixed noise suppression for outdoor target detection based on rotor Unmanned Aerial Vehicle (UAV), a mixed noise suppression algorithm based on Developable Local Surface (DLS) is proposed. This algorithm realizes the complementary advantages of the developable local surface algorithm and the layered noise reduction algorithm, and achieves the noise reduction effect that the neither algorithm can reach. Firstly, the developable local surface of image is used to suppress salt & pepper noise and low-density Gaussian noise in the image to obtain a preliminary denoised image. Then, the layered noise reduction in the spatial domain and the Fourier domain is carried, removing Gaussian noise and maximize the preservation of image edges, textures and other details. Finally, iteratively developable local surface and layered noise reduction to remove further residual components of mixed noise to achieve the purpose of suppressing mixed noise in target detection images. The experimental results show that the proposed algorithm has certain advantages over the other seven algorithms in removing mixed noise, and its subjective visual index and objective data index statistics are superior to those of the other seven algorithms.
Key words:Image denoising/
Mixed noise/
Developable local surface/
Layered noise reduction/
Iteratively denoising
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