<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>For the complex background of traditional radar image target detection methods is affected by multiple interferers, which leads to a low recognition rate of multi-target classification and a high false alarm rate. In this paper, the current hot research BM3D denoising method is introduced into radar image target detection. Firstly, the detection process of the BM3D algorithm is constructed, and the image pixels are reasonably clustered by denoising so that the image blocks in homogeneous regions have a high matching degree and those in non-homogeneous regions have a low matching degree. Secondly, the clustering recognition feature is used to limit the search range of similar blocks to homogeneous regions, to improve the denoising efficiency of the algorithm. Finally, the simulation results of this paper show that compared with previous algorithms, the BM3D detection method has significantly improved in both the subjective visual effect map and objective numerical indexes. For example, the image denoising quality is improved by about 0.716dB on average and up to 1.031dB, the image denoising time is shortened by about 19.962s on average, and the algorithm runs 1.211 times faster than the original algorithm. It is proved that the algorithm can improve denoising efficiency and reduce computational complexity while enhancing detailed information.</p>
</abstract>

For the complex background of traditional radar image target detection methods is affected by multiple interferers, which leads to a low recognition rate of multi-target classification and a high false alarm rate. In this paper, the current hot research BM3D denoising method is introduced into radar image target detection. Firstly, the detection process of the BM3D algorithm is constructed, and the image pixels are reasonably clustered by denoising so that the image blocks in homogeneous regions have a high matching degree and those in non-homogeneous regions have a low matching degree. Secondly, the clustering recognition feature is used to limit the search range of similar blocks to homogeneous regions, to improve the denoising efficiency of the algorithm. Finally, the simulation results of this paper show that compared with previous algorithms, the BM3D detection method has significantly improved in both the subjective visual effect map and objective numerical indexes. For example, the image denoising quality is improved by about 0.716dB on average and up to 1.031dB, the image denoising time is shortened by about 19.962s on average, and the algorithm runs 1.211 times faster than the original algorithm. It is proved that the algorithm can improve denoising efficiency and reduce computational complexity while enhancing detailed information.

BM3D denoising-based multi-target detection method for complex background radar images

Hunan University of Science and Engineering

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

For the complex background of traditional radar image target detection methods is affected by multiple interferers, which leads to a low recognition rate of...

BM3D denoising-based multi-target detection method for complex background radar images

Publié en ligne: 16 mai 2023

Pages: -

Reçu: 17 juin 2022

Accepté: 07 nov. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00255

Mots clés
BM3D algorithm, Radar image, Clustering calculation, Denoising efficiency, Multi-target detection

© 2023 Hongzhi Ma, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

BM3D denoising-based multi-target detection method for complex background radar images

Publié en ligne: 16 mai 2023

Pages: -

Reçu: 17 juin 2022

Accepté: 07 nov. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00255

Mots clésBM3D algorithm, Radar image, Clustering calculation, Denoising efficiency, Multi-target detection

© 2023 Hongzhi Ma, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
BM3D algorithm, Radar image, Clustering calculation, Denoising efficiency, Multi-target detection