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

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

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

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

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.

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

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

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

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

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.

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

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

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

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.

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

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

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

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

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.

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

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

Ms. Jin Yang

Dr. Yuehua Li