PIER B
 
Progress In Electromagnetics Research B
ISSN: 1937-6472
Home | Search | Notification | Authors | Submission | PIERS Home | EM Academy
Home > Vol. 57 > pp. 63-73

PASSIVE MILLIMETER WAVE IMAGE DENOISING BASED ON ADAPTIVE MANIFOLDS

By S. Zhu, Y. Li, J. Chen, and Y. Li

Full Article PDF (411 KB)

Abstract:
Since the characters of poor inherent resolution and low signal-to-noise limit the application of the passive millimeter wave (PMMW) image, it is particularly important to improve the resolution and denoise the PMMW image. In this paper, the adaptive manifolds filtering algorithm based on non-local means (AM-NLM) is illustrated in detail. And an improved version of AM-NLM filtering algorithm is proposed for processing the PMMW image. The proposed algorithm firstly applies the AM-NLM filtering to obtain the basic denoised PMMW image. Then the image enhancement based on Laplacian of Gaussian operator is performed to enhance the edge of the target in PMMW image. Finally, the hard-threshold filtering with different thresholds is adopted to filter each dimension to achieve the final filtering response. Experimental results have shown that the proposed PMMW filtering algorithm has better and more satisfactory performance compared to AM-NLM, both in subjective visual effect and objective image quality metric. Additionally, our proposed algorithm is also available for real PMMW images.

Citation:
S. Zhu, 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

References:
1. Tuovinen, J., N. Hughes, P. Jukkala., P. Kangaslathti, T. Karttaavi, P. Sjoman, and J. Varis, "Technology for millimeter wave radiometers," IEEE Transactions on Microwave Theory and Techniques , Vol. 2, 883-886, 2003.

2. Bonafoni, S., F. Alimenti, G. Angelucci, and G. Tasselli, "Microwave radiometry imaging for forest fire detection: A simulation study," Progress In Electromagnetics Research, Vol. 112, 77-92, 2011.

3. Ruf, C. S., C. T. Swift, A. B. Tanner, and D. M. Le Vine, "Interferometric synthetic aperture microwave radiometry for the remote sensing of the earth," IEEE Transactions on Geoscience and Remote Sensing, Vol. 26, No. 5, 597-611, 1988.
doi:10.1109/36.7685

4. Histace, A., Image Restoration --- Recent Advances and Applications, InTech, Morn Hill, 2012.
doi:10.5772/2389

5. Wang, B. and X. Li, "Near range millimeter wave radiometer passive image high resolution restoration," IEEE Global Symposium on Milimeter Waves, 325-328, 2008.

6. Reeves, S. J., "Analysis of the difficulties and possibilities for super-resolution," Proc. SPIE, Vol. 3064, 239-248, 1997.
doi:10.1117/12.277086

7. Zheng, X. and J. Yang, "Adaptive projected landweber super-resolution algorithm for passive millimeter wave imaging," Proc. SPIE, Vol. 6787, 1001-1007, 2007.

8. Lucy, L. B., "An iteration technique for the rectification of observed distributions," Astronomical Journal, Vol. 79, 745, 1974.
doi:10.1086/111605

9. Richardson, W. H. , "Bayesian-based iterative method of image restoration," Journal of the Optical Society of America, Vol. 62, 55-59, 1972.
doi:10.1364/JOSA.62.000055

10. Hunt, B. R. and P. Sementilli, "Description of a poisson imagery super resolution algorithm," Astronomical Data Analysis Software and Systems I, Vol. 52, 196-799, 1992.

11. Li, L., J. Yang, G. Cui, J. Wu, Z. Jiang, and X. Zheng, "Super-resolution processing of passive millimeter wave image based on conjugate-gradient algorithm," Journal of Systems Engineering and Electronics, Vol. 20, No. 4, 762-767, 2009.

12. Xiao, Z., J. Xu, and S. Peng, "Super resolution image restoration of a PMMW sensor based on POCS algorithm," Systems and Control in Aerospace and Astronautics, 680-683, 2006.

13. Park, H., S. Kim, M. K. Singh, J. Choi, H. Lee, and Y. Kim, "Performance of wavelet based restoration for passive millimeter-wave images," Proc. SPIE, Vol. 5879, 157-166, 2005.
doi:10.1117/12.602667

14. Zhang, Q., Y. Fu, L. Li, and J. Yang, "A millimeter-wave image denoising method bsed on adaptive sparse representation," IEEE International Conference on Computational Problem-Solving, 652-655, 2011.

15. Gastal, E. S. L. and M. M. Oliveira, "Adaptive manifolds for real-time high-dimensional filtering," Proc. SIGGRAPH, Vol. 31, No. 4, 2012.

16. Tasdizen, T., "Principal components for non-local means image denoising," IEEE International Conference on Image Processing, 1728-1731, 2008.

17. 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.

18. Wang, Z. and E. P. Simoncelli, "Stimulus synthesis for efficient evaluation and refinement of perceptual image quality metrics," Proc. SPIE, Vol. 5292, 99-108, 2004.
doi:10.1117/12.537129


© Copyright 2010 EMW Publishing. All Rights Reserved