Local Orientation Field Based Nonlocal Means Method for Fingerprint Image De-Noising

doi:10.4236/jsip.2013.43B026

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Journal of Signal and Information Processing, 2013, 4, 150-153

doi:10.4236/jsip.2013.43B026 Published Online August 2013 (http://www.scirp.org/journal/jsip)

Local Orientation Field Based Nonlocal Means Method fo r

Fingerprint Image De-Noising

J. Zou, J. B. Feng, X. M. Zhang, M. Y. Ding

School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China.

Email: [email protected]

Received May, 2013.

ABSTRACT

The de-noising of the fingerprint image is one of the key tasks before the extraction of the minutiae in automatic finger-

print matching. When used for de-noising the fingerprint image, the nonlocal means method can not preserve the local

minutiae in the fingerprint image very well. To address this problem, we propose a local orientation field based nonlo-

cal means (NLM-LOF) method in this paper. Experimental results on the simulated and real images show that the pro-

posed method can suppress noise effectively while preserving edges and details in the fingerprint image and it outper-

forms the state-of-art nonlocal means method in terms of qualitative metrics and visual comparisons.

Keywords: Fingerprint Image Denoising; Nonlocal Means Filtering; Orientation Field

1. Introduction

As one of the most important biometric technologies,

fingerprint identification has been widely used in identity

recognition. Usually, fingerprint identification relies

heavily on the performance of the minutiae extraction

algorithm[1]. However, due to complex identify condi-

tio- ns, the acquired fingerprint images are usually con-

taminated with noise which is disadvantageous for the

minutiae extraction. So it is desirable and crucial to de-

sign a robust filter to preserve the local minutiae and

improve the clarity of the ridge structures.

Many de-noising algorithms have already been pre-

sented to remove noise in the fingerprint images. Liang,

et.al [2] proposed the morphological amoebas method to

simultaneously reduce noise and preserve useful details

with the help of pilot images from canny edge detection.

Liang, et.al [3] developed a combinatorial linear time

algorithm to eliminate impulsive noise and useless com-

ponents from fingerprint images using Euclidean dis-

tance transform. In [4], Bayesian de-noising in the wave-

let domain was presented to realize fingerprint image

de-noising. All these methods tend to damage edges and

details in the fingerprint images because they only use

local information in the images.

Different from the above mentioned methods, the

non-local means filter recently proposed by Buades [5]

takes advantage of the redundancy of similar patches in

the images and estimates the considered pixel with a

weighted average of all the pixels in its neighborhood or

the whole image. However, the performance of the tradi-

tional non-local means (TNLM) method will be greatly

influenced by similarity window and similarity computa-

tion method. Many novel solutions have been proposed

to address this problem such as the NLM-

Reprojections (NLM-R) method [6], the NLM using

Shape Adaptive Patches (NLM-SAP) [7]. Although these

improved methods perform better than the traditional

NLM, they cannot preserve fringes and minutiaes effec-

tively. In this paper, we propose a novel nonlocal means

filter based on the estimation of the orientation field.

Compared with above state-of-art de-noising methods,

the proposed method is more robust and it can preserve

minutiae better while suppressing noise in the fingerprint

images.

2. Our Method

In the TNLM method, for the considered pixel (m,n) in

the noisy image, the corresponding non-local means

de-noised intensity NL(m,n) in the search window Ω is

calculated as [5]:

(,)

(,,,)(,)

(,) (,,,)

pq pq

wmnpqvpq

NL m nwmnpq



 (1)

where denotes the intensity of pixel (p,q),

w(m,n,p,q) denotes the similarity of two pixels (m,n) and

(p,q) and it is computed as:

v(p,q)

(,) (.)

(,,,)

ssa

vmnvpq

wmnpq e



 (2)

Local Orientation Field Based Nonlocal Means Method for Fingerprint Image De-Noising 151

where means the decay parameter .and

denote the intensities of similarity windows s

centered at (m,n) and (p,q).

), q),( nmvs

(pvs

a,2denotes the Gaussian

weighted Euclidean distance convolved with a Gaussian

kernel of standard deviation a. As we can see, the TNLM

method utilizes the geometrical configuration in a neigh-

bourhood to determine the pixel similarity, but its igno-

rance of the orientation field will degrade its perform-

ance in restoring the fingerprint image. Considering that

a well estimated orientation field can facilitate repre-

senting geometrical features effectively, we introduce the

orientation field into the traditional nonlocal means to

obtain improved de-noised results.The local orientation

of each block centered at pixel (m, n) is estimated as [1]:



(,)2 (,)(,)









 

Vmnuvuv

um vn y



(3)



∂∂



m+ n+

2222

V(m,n)=(u,v)-(u,v)

yxy

u=m- v=n-

(4)





(,)

(,)tan

2(,)





Vmn

mn Vmn

x (5)

where and denote the gradients of pixel

(m,n),which is calculated by Sobel operator[8].

(,)Vmn

y(,)Vmn

(,)



mnis

the least square estimate of the local ridge orientation of

the block W centered at pixel (m,n). Then we adjust the

weigh{( using orientation information

(,)

)} pq

,,wmnp,q



,2),(),(

).(),(

),,,( h

qpnm

qpvnmv

eeqpnmw

ss 



(6)

where 2

his the decay parameter. It can be seen from (6)

that different from the TNLM method, the proposed

method determines the weight by utilizing both the gray

values of image patches and the local orientation field.

To demonstrate the superiority of the proposed method

in weight calculation, we use the noisy images with

standard deviation σϵ{50,60,70} shown in Figure 1(a)

and compute the distribution of the similarity between

the center pixel and other pixels in each test image. Fig-

ure 1(b) and Figure 1(c) shows the weight distribution

for the two compared methods, where its values go from

one (white) to zero (black). It can be seen from Figure 1

that our method can determine the similarity more effec-

tively than the TNLM method, especially at high noise

corruption.

3. Experiments

In this section, the fingerprint image simulated using

fingerprint generator software released at http://www.ia.c

as.cn/kygz/kycg/rjdj/200910/t20091010_2542737.html

and the real fingerprint image are used as the test images.

For simulation experiments, gaussian white noise with

the standard deviation



ϵ{20,30,40,50} is added to the

simulated fingerprint image. The restoration performance

of the TNLM, the NLM-R, the NLM-SAP and the pro-

posed NLM-LOF is appreciated using peak signal-to-

noise ratio (PSNR) and structural similarity

σ = 50 σ = 60 σ = 70

(a)

σ = 50 σ = 60 σ = 70

(b)

σ = 50 σ = 60 σ = 70

(c)

Figure 1. The distribution of weight similarity between the

center pixel and other pixels in the noisy images. (a). noisy

images with different standard deviations, (b). the weight

distribution for the TNLM method, (c). the weight distribu-

tion for the NLM-LOF.

index [8] (SSIM). In all the experiments, we choose the

search window size and the similarity window size to be

5×5 and 3×3, respectively. The filtering parameter h (the

same for h1 in the NLM-LOF) is fixed to 6*



and 2 is

tuned to obtain the best restoration performance for the

proposed NLM-LOF. Table 1 lists the PSNR and SSIM

values of all the de-noising methods operating on the

corrupted fingerprint image. The observation from Table

1 demonstrates that our method has the best restoration

performance in that it outperforms the compared methods

in terms of the PSNR and SSIM measurement.

To visualize such improvement, we illustrate the

de-noised images in Figure 2. As we can see, Figure 2(d)

is more similar to the ground truth than Figure 2(c) and

the edges are significantly protected while the TNLM

(Figure 2(c)) oversmooth the whole image.

Local Orientation Field Based Nonlocal Means Method for Fingerprint Image De-Noising

152

Besides, we test the two methods on a real fingerprint

image shown in Figure 3(a). Figure 3(b) is the zoomed

view of labeled region In Figure 3(a). Much noise still

exists in the image de-noised by the TNLM method (Fig-

ure 3(b)) and the important anatomical details are

blurred. By comparison, the NLM-LOF can suppress

noise effective- ly while preserving the anatomical de-

tails very well as shown in Figure 3(d).

4. Conclusions

We have proposed the local orientation field based

nonlocal means method. Compared with the state-of-art

nonlocal means filter, the proposed method can de-noise

the fingerprint images more effectively because it utilizes

more feature information to represent the structural simi-

larity of pixels in the image. Experiments on simulated

and real fingerprint images demonstrate the effectiveness

of the proposed method in restoring the corrupted finger-

print image in terms of noise reduction and detail pres-

ervation.

Table 1. Comparisons of the PSNR values and SSIM value-

sof the de-noised results on different standards noisy im-

ages.

(a) (b)

Figure 2. De-noising experiment on a simulated image. a.

the original image, b. noisy image (), c. result with

NLM, d. result with NLM-LOF.

40



(

a) (b)

Figure 3. Comparisons of the de-noising results for the

TNLM and NLM-LOF operating on a real fingerprint im-

age. a.original image, b. zoomed view of the labeled region,

c. result with TNLM, d.result with NLM-LOF.

5. Acknowledgements

This work was partly supported by the National Natural

Science Foundation of China (Grant No.:30911120497),

the National 973 project (Grant No.: 2011CB933103),

the Project of the National 12th-Five Year Research Pro-

gram of China (Grant No.: 2012BAI13B02) and Project

of National High Technology Research and Development

Program of China（Grant No.:2013AA040206).

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