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Scattering removal for finger-vein image restoration.

Yang J, Zhang B, Shi Y - Sensors (Basel) (2012)

Bottom Line: To give a proper description of finger-vein image degradation, a biological optical model (BOM) specific to finger-vein imaging is proposed according to the principle of light propagation in biological tissues.Based on BOM, the light scattering component is sensibly estimated and properly removed for finger-vein image restoration.Finally, experimental results demonstrate that the proposed method is powerful in enhancing the finger-vein image contrast and in improving the finger-vein image matching accuracy.

View Article: PubMed Central - PubMed

Affiliation: Tianjin Key Laboratory for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China. jfyang@cauc.edu.cn

ABSTRACT
Finger-vein recognition has received increased attention recently. However, the finger-vein images are always captured in poor quality. This certainly makes finger-vein feature representation unreliable, and further impairs the accuracy of finger-vein recognition. In this paper, we first give an analysis of the intrinsic factors causing finger-vein image degradation, and then propose a simple but effective image restoration method based on scattering removal. To give a proper description of finger-vein image degradation, a biological optical model (BOM) specific to finger-vein imaging is proposed according to the principle of light propagation in biological tissues. Based on BOM, the light scattering component is sensibly estimated and properly removed for finger-vein image restoration. Finally, experimental results demonstrate that the proposed method is powerful in enhancing the finger-vein image contrast and in improving the finger-vein image matching accuracy.

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Comparisons with other methods. (a) Some captured finger-vein images. (b) The results from histogram template equalization (HTE) [5]. (c) The results from high frequency emphasis filtering (HFEF) [13]. (d) The results from circular Gabor filtering (CGF) [7]. (e) The results from image dehazing (ImD) [19]. (f) The results from the proposed method.
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f10-sensors-12-03627: Comparisons with other methods. (a) Some captured finger-vein images. (b) The results from histogram template equalization (HTE) [5]. (c) The results from high frequency emphasis filtering (HFEF) [13]. (d) The results from circular Gabor filtering (CGF) [7]. (e) The results from image dehazing (ImD) [19]. (f) The results from the proposed method.

Mentions: In Figure 10, we compare our method with several common approaches for finger-vein image enhancement. Additionally, we treat the degraded finger-vein images as hazing images, and directly use dehazing method to restore them regardless of the mismatch between the Koschmieder model and the proposed model. Here, a method proposed in [19] is adopted to implement finger-vein image “dehazing”, and the results are also shown in Figure 10.


Scattering removal for finger-vein image restoration.

Yang J, Zhang B, Shi Y - Sensors (Basel) (2012)

Comparisons with other methods. (a) Some captured finger-vein images. (b) The results from histogram template equalization (HTE) [5]. (c) The results from high frequency emphasis filtering (HFEF) [13]. (d) The results from circular Gabor filtering (CGF) [7]. (e) The results from image dehazing (ImD) [19]. (f) The results from the proposed method.
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC3376557&req=5

f10-sensors-12-03627: Comparisons with other methods. (a) Some captured finger-vein images. (b) The results from histogram template equalization (HTE) [5]. (c) The results from high frequency emphasis filtering (HFEF) [13]. (d) The results from circular Gabor filtering (CGF) [7]. (e) The results from image dehazing (ImD) [19]. (f) The results from the proposed method.
Mentions: In Figure 10, we compare our method with several common approaches for finger-vein image enhancement. Additionally, we treat the degraded finger-vein images as hazing images, and directly use dehazing method to restore them regardless of the mismatch between the Koschmieder model and the proposed model. Here, a method proposed in [19] is adopted to implement finger-vein image “dehazing”, and the results are also shown in Figure 10.

Bottom Line: To give a proper description of finger-vein image degradation, a biological optical model (BOM) specific to finger-vein imaging is proposed according to the principle of light propagation in biological tissues.Based on BOM, the light scattering component is sensibly estimated and properly removed for finger-vein image restoration.Finally, experimental results demonstrate that the proposed method is powerful in enhancing the finger-vein image contrast and in improving the finger-vein image matching accuracy.

View Article: PubMed Central - PubMed

Affiliation: Tianjin Key Laboratory for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China. jfyang@cauc.edu.cn

ABSTRACT
Finger-vein recognition has received increased attention recently. However, the finger-vein images are always captured in poor quality. This certainly makes finger-vein feature representation unreliable, and further impairs the accuracy of finger-vein recognition. In this paper, we first give an analysis of the intrinsic factors causing finger-vein image degradation, and then propose a simple but effective image restoration method based on scattering removal. To give a proper description of finger-vein image degradation, a biological optical model (BOM) specific to finger-vein imaging is proposed according to the principle of light propagation in biological tissues. Based on BOM, the light scattering component is sensibly estimated and properly removed for finger-vein image restoration. Finally, experimental results demonstrate that the proposed method is powerful in enhancing the finger-vein image contrast and in improving the finger-vein image matching accuracy.

Show MeSH