volume | PIER Journals

Abstract

To solve the problem of mixed noise in a passive millimeter-wave (PMMW) imaging system that affects object detection, recognition, and classification, this paper proposes a blind denoising algorithm based on pixel non-local self-similarity (PNSS) prior to PMMW images. Firstly, an adaptive filtering algorithm is introduced, utilizing PNSS prior to estimating the noise intensity and improving the problem of noise residual caused by parameter uncertainty in traditional filtering processes. Secondly, a three-level joint denoising algorithm is developed, accompanied by an iterative regression algorithm to effectively filter the mixed noise in PMMW images while preserving image contours. Finally, the effectiveness of the proposed method is demonstrated through a comparison with patch similarity-based prior denoising methods and high-dimensional mixed noise denoising methods. Experimental results substantiate that the proposed PNSS blind denoising method successfully suppresses mixed noise in PMMW images, enhances subjective visual perception, and presents a novel approach for denoising under various PMMW imaging mechanisms.

1. Chen, J., Y. Li, J. Wang, Y. Li, and Y. Zhang, "An accurate imaging algorithm for millimeter wave synthetic aperture imaging radiometer in near-field," Progress In Electromagnetics Research, Vol. 141, 517-535, 2013.
doi:10.2528/PIER13060702 Google Scholar

2. Zhu, S., Y. Li, J. Chen, and Y. Li, "Passive millimeter wave image denoising based on adaptive manifolds," Progress In Electromagnetics Research B, Vol. 57, 63-73, 2014.
doi:10.2528/PIERB13092608 Google Scholar

3. Cheng, Y., Y. Wang, Y. Niu, H. Rutt, and Z. Zhao, "Physically based object contour edge display using adjustable linear polarization ratio for passive millimeter-wave security imaging," IEEE Transactions on Geoscience and Remote Sensing, 1-15, 2020.
doi:10.1080/15481603.2019.1650447 Google Scholar

4. Peng, R., J. Chen, Z. Liu, and Z. Guo, "Millimeter wave image super resolution using multichannel depth convolution neural network," Progress In Electromagnetics Research M, Vol. 113, 225-235, 2022.
doi:10.2528/PIERM22070801 Google Scholar

5. Yang, H., Z. Yang, A. Hu, C. Liu, T. J. Cui, and J. Miao, "Source-free domain adaptive detection of concealed objects in passive millimeter-wave images," IEEE Transactions on Instrumentation and Measurement, Vol. 72, No. 5005015, 1-15, 2023. Google Scholar

6. Fu, P., D. Zhu, F. Hu, Y. Xu, and H. Xia, "A near-field imaging algorithm based on angular spectrum theory for synthetic aperture interferometric radiometer," IEEE Transactions on Microwave Theory and Techniques, Vol. 70, No. 7, 3606-3616, 2022.
doi:10.1109/TMTT.2022.3175156 Google Scholar

7. Sun, D., Y. Shi, and Y. Feng, "Blind deblurring and denoising via a learning deep CNN denoiser prior and an adaptive L0-regularised gradient prior for passive millimetre-wave images," IET Image Processing, Vol. 14, No. 17, 4774-4784, 2020.
doi:10.1049/iet-ipr.2020.1193 Google Scholar

8. Sarkis, M., "Adaptive reconstruction of millimeter-wave radiometric images," IEEE Transactions on Image Processing, Vol. 21, No. 9, 4141-4151, 2012.
doi:10.1109/TIP.2012.2198219 Google Scholar

9. Chen, J., Y. Li, J. Wang, Y. Li, and Y. Zhang, "Adaptive CLEAN algorithm for millimeter wave synthetic aperture imaging radiometer in near field," IET Image Processing, Vol. 9, No. 3, 218-225, 2015.
doi:10.1049/iet-ipr.2014.0443 Google Scholar

10. Zhao, Y., W. Si, A. Hu, and J. Miao, "A real-time calibration method of visibility function for passive millimeter wave imaging," IEEE International Conference on Microwave and Millimeter Wave Technology, 2020. Google Scholar

11. Li, Y. and Y. Li, "Passive millimeter-wave image denoising based on improved algorithm of non-local mean," International Journal of Advancements in Computing Technology, Vol. 4, No. 10, 158-164, 2012.
doi:10.4156/ijact.vol4.issue10.19 Google Scholar

12. Li, Y., Y. Li, H. Su, Z. Li, and S. Zhu, "Passive millimeter wave image denoising based on improved version of BM3D," Advances in Information Sciences & Service Sciences, Vol. 4, No. 22, 106-113, 2012.
doi:10.4156/aiss.vol4.issue22.14 Google Scholar

13. Zhu, S., Y. Li, and Y. Li, "A PMMW image denoising based on adaptive manifolds and high-dimensional mean median filter," Optik, Vol. 126, No. 24, 5624-5628, 2015.
doi:10.1016/j.ijleo.2015.09.089 Google Scholar

14. Buades, A., B. Coll, and J. M. Morel, "A non-local algorithm for image denoising," IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2, 60-65, 2005. Google Scholar

15. Dabov, K., A. Foi, V. Katkovnik, and K. Egiazarian, "Image denoising by sparse 3-D transform-domain collaborative filtering," IEEE Transactions on Image Processing, Vol. 16, No. 8, 2080-2095, 2007.
doi:10.1109/TIP.2007.901238 Google Scholar

16. Hou, Y., J. Xu, M. Liu, G. Liu, L. Liu, F. Zhu, and L. Shao, "NLH: A blind pixel-level non-local method for real-world image denoising," IEEE Transactions on Image Processing, Vol. 29, 5121-5135, 2020.
doi:10.1109/TIP.2020.2980116 Google Scholar

17. Hou, H., Y. Shao, Y. Geng, Y. Hou, P. Ding, and B. Wei, "PNCS: Pixel-level non-local method based compressed sensing undersampled MRI image reconstruction," IEEE Access, Vol. 11, 42389-42402, 2023.
doi:10.1109/ACCESS.2023.3270900 Google Scholar

18. Xu, J., Z.-A. Liu, Y.-K. Hou, X.-T. Zhen, L. Shao, and M.-M. Cheng, "Pixel-level non-local image smoothing with objective evaluation," IEEE Transactions on Multimedia, Vol. 23, 4065-4078, 2021.
doi:10.1109/TMM.2020.3037535 Google Scholar

19. Zhu, R., X. Li, Y. Wang, and X. Zhang, "Medical image fusion based on pixel-level nonlocal self-similarity prior and optimization," International Conference on Database Systems for Advanced Applications, 2022. Google Scholar

20. Sweldens, W., "The lifting scheme: A custom-design construction of biorthogonal wavelets," Applied and Computational Harmonic Analysis, Vol. 3, No. 2, 186-200, 1996.
doi:10.1006/acha.1996.0015 Google Scholar

21. Wang, Z., A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: From error visibility to structural similarity," IEEE Transactions on Image Processing, Vol. 13, No. 4, 600-612, 2004.
doi:10.1109/TIP.2003.819861 Google Scholar

Ms. Jin Yang

Dr. Yuehua Li