A novel denoising method for infrared image based on bilateral filtering and non-local means

Feng-lian Liu , Meng-yao Sun , Wen-na Cai

Optoelectronics Letters ›› 2017, Vol. 13 ›› Issue (3) : 237 -240.

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Optoelectronics Letters ›› 2017, Vol. 13 ›› Issue (3) :237 -240. DOI: 10.1007/s11801-017-7007-8
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A novel denoising method for infrared image based on bilateral filtering and non-local means
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Abstract

This paper presents an image denoising method based on bilateral filtering and non-local means. The non-local region texture or structure of the image has the characteristics of repetition, which can be used to effectively preserve the edge and detail of the image. And compared with classical methods, bilateral filtering method has a better performance in denosing for the reason that the weight includes the geometric closeness factor and the intensity similarity factor. We combine the geometric closeness factor with the weight of non-local means, and construct a new weight. Experimental results show that the modified algorithm can achieve better performance. And it can protect the image detail and structure information better.

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Feng-lian Liu, Meng-yao Sun, Wen-na Cai. A novel denoising method for infrared image based on bilateral filtering and non-local means. Optoelectronics Letters, 2017, 13(3): 237-240 DOI:10.1007/s11801-017-7007-8

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References

[1]

Yoshizawa S, Belyaev A, Yokota H. Computer Graphics Forum. 2010, 29: 60

[2]

Li Y J, Yan L, Yang B. Open Automation and Control Systems Journal. 2015, 7: 275

[3]

Mairal J, Bach F, Ponce J, Sapiro G, Zisserman A. Non-Local Sparse Models for Image Restoration. IEEE International Conference on Computer Vision. 2009, 30: 2272

[4]

Dabov K, Foi A, Katkovnik V. Egiazarian Karen. IEEE Transactions on Image Processing. 2007, 16: 2080

[5]

LI Z, LIU W-j, RONG M-t, LIU T-z. Information Technology. 2012, 4: 30

[6]

Danielyan A, Katkovnik V, Egiazarian K. IEEE Transactions on Image Processing. 2012, 21: 1715

[7]

Dai L, Zhang Y, Li Y. International Journal of Signal Processing. Image Processing and Pattern Recognition. 2013, 6: 41

[8]

Luo X-G, J-R, Wang H-J, Yang Q. Journal of the University of Electronic Science and Technology of China. 2015, 44: 84

[9]

Thaipanich T, Oh B T, Wu P-H, Xu D, Kuo C.-C J. IEEE Transactions on Consumer Electronics. 2010, 56: 2623

[10]

Wu Y, Dai Y, Yin J, Wu J. Transactions of Tianjin University. 2015, 21: 104

[11]

Shreyamsha Kumar B K. Signal, Image and Video Processing. 2013, 7: 1211

[12]

Gan K, Tan J, He L. Non-Local Means Image Denoising Algorithm Based on Edge Detection. 5th International Conference on Digital Home. 2014117

[13]

Gao Z-l, Ye W-l, Zheng C-t, Wang Y-d. Optoelectronics Letters. 2014, 10: 299

[14]

Zhao D-x, Liu P-j, Zhang D-g. Optoelectronics Letters. 2014, 10: 477

[15]

Kizilkaya A, Elbi M D. IETE Journal of Research. 2016, 62: 605

[16]

Ikemoto Y, Sekiyama K. Journal of Ad vanced Computational Intelligence and Intelligent Informatics. 2016, 20: 705

[17]

Jonatas L d P, Claudio F M T, Helio P. Applied Soft Computing. 2016, 46: 778

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