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: xmboshi.zhang@gmail.com
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)
2,
2
1
(,) (.)
(,,,)
ssa
vmnvpq
h
wmnpq e
(2)
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