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Ms. Rutu Agrawal et al. Int. Journal of Engineering Research and Application
ISSN : 2248-9622, Vol. 3, Issue 5, Sep-Oct 2013, pp.963-966

RESEARCH ARTICLE

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OPEN ACCESS

Adaptive Filter Based On TDBLMS Algorithm for Image Noise
Cancellation
Ms. Rutu Agrawal*, Dr. A. J. Patil**, Mr. C. S. Patil***
*PG student, Dept. of EXTC, North Maharashtra University,S.G.D.C.O.E, Jalgaon, India.
** Dept. of EXTC, North Maharashtra University, S.G.D.C.O.E, Jalgaon, India.
*** Dept. of EXTC, North Maharashtra University, S.G.D.C.O.E Jalgaon, India.

Abstract
Images are often degraded by noises. Noise can occur during image capture, transmission, etc. Noise removal is
an important task in image processing. In general the results of the noise removal have a strong influence on the
quality of the image processing technique. Several techniques for noise removal are well established in color
image processing. The nature of the noise removal problem depends on the type of the noise corrupting the
image. An adaptive filter for two-dimensional block processing in image noise cancellation is proposed in this
paper. The processing includes two phases. They are the weight-training phase and the block-adaptation phase.
The weight-training phase obtains the suitable weight matrix to be the initial one for the block-adaptation phase
such that a higher signal-to-noise ratio can be achieved. To verify the feasibility of this approach, the simulation
with the block sizes of 4 x 4, 8 x 8, 16 x 16, and 32 x 32 are performed. The simulation results show that this
approach performs well.
Keywords: Adaptive filter, Adaptive algorithm, Least squares approximation, Noise cancellation, PSNR.

I.

INTRODUCTION

Noise is the result of errors in the image
acquisition process that results in pixel values that do
not reflect the true intensities of the real scene. Noise
reduction is the process of removing noise from a
signal. Noise reduction techniques are conceptually
very similar regardless of the signal being processed,
however a priori knowledge of the characteristics of
an expected signal can mean the implementations of
these techniques vary greatly depending on the type
of signal. The image captured by the sensor
undergoes filtering by different smoothing filters and
the resultant images. All recording devices, both
analogue and digital, have traits which make them
susceptible to noise. The fundamental problem of
image processing is to reduce noise from a digital
color image. The two most commonly occurring
types of noise are (i) Impulse noise, ii) Additive
noise (e.g. Gaussian noise) and iii) Multiplicative
noise (e.g. Speckle noise).
Many methods have been widely used to
eliminate noise like linear and nonlinear filtering
methods, adaptive noise cancellation.
1.1 Adaptive Filtering
An adaptive filter is a filter that self-adjusts
its transfer function according to an optimization
algorithm driven by an error signal. Because of the
complexity of the optimization algorithms, most
adaptive filters are digital filters. Adaptive filters that
are well-known as the filters with the coefficients
adjusted by the adaptive algorithms are widely used
in various applications for achieving a better
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performance. The dimension of the adaptive filters
varies from application to application.
In the fields of digital signal processing and
communication such as the system identification,
echo cancellation, noise canceling, and channel
equalization [2]-[6], the one dimensional (1-D)
adaptive algorithms are generally adopted. The 1-D
adaptive algorithms are usually classified into two
families. One is the least-mean-square (LMS) family;
the other is the recursive-least-square (RLS) family.
The algorithms in the LMS family have the
characteristics of easy implementation and low
computational complexity [1]. In 1981, Clark [7]
proposed the block least-mean-square (BLMS)
approach which is an application extended from the
block processing scheme proposed by Burrus [8]. In
such an approach, the computational complexity is
dramatically reduced. In addition, the linear
convolution operation can be accomplished by
parallel processing or fast Fourier transforms (FFT).
In the applications of digital image
processing, two dimensional (2-D) adaptive
algorithms such as TDLMS, OBA, OBAI, TDBLMS
and TDOBSG are usually used [9]-[12].Either in
TDLMS or TDBLMS, the convergence factors are
constant. Instead of the constant convergence factors
in TDLMS and TDBLMS, the space-varying
convergence factors are used in OBA, OBAI, and
TDOBSG for better convergence performance.
However, such space-varying convergence
factors will increase the computational complexity
due to the computations for the new convergence
factor of next block.
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In this paper, we proposed an adaptive filter
with weight training mechanism by finding a suitable
weight (coefficient) matrix for the digital filter in
advance. Then, treat this weight matrix as the initial
weight matrix for the processing of noise
cancellation.

II.

ADAPTIVE ALGORITHM

Adaptive algorithms are used to adjust the
coefficients of the digital filter such that the error
signal is minimized according to some criterion.
2.1 2-D Block LMS Algorithm
A 2-D signal is partitioned into blocks with
a dimension of L x L for each in the 2-D disjoint
block-by-block image processing. An image with R
rows of pixel and G columns of pixel partitioned into
R/L * C/L blocks is illustrated in Fig.1.

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approximation step of fixed-filter design. The error is
then used to form a performance function or
objective function that is required by the adaptation
algorithm in order to determine the appropriate
updating of the filter coefficients. The minimization
of the objective function implies that the adaptive
filter output signal is matching the desired signal in
some sense. Fig. 2 illustrates this approach which
performs the operations from (3) to (5) iteratively
[10]. That is
M N
∑ ∑
i=1 j=1
M
N
= ∑ ∑
i=1 j=1
(3)
where
is the image of the S-th block after
processing, Ws(i,j) is the (i,j)-th element in the
weight matrix Ws of the S-th block. The error signal
is then obtained by subtracting the image
from the primary input image
.
That is
(4)
The weight matrix
then updated by

the (S + l)-th block is
L
L
∑ ∑

Figure 1. 2-D block-by-block processing with
disjoint square blocks of a dimension L x L.
The block index S and the spatial block
index (r, c) is related by [12]

,
where µ is the convergence factor.

(5)

(1)
where r = 1, 2,… R/L and c = 1, 2,…C/L.
For convenient, the (r, c)-th element d(r, c) of
the image can be treated as the (rb, cb)-th element in
the S-th block and denoted as the element ds (rb,cb).
The relationship is
(2)
where rb = 1,2,..,L and cb = 1,2,…,L. The block
processing is started by processing the image blockby-block sequentially from left to right and from top
to bottom in which each pixel is convolved the pixel
in a filter window with a dimension of M x N.
Adaptive filtering can be considered as a
process in which the parameters used for the
processing of signals changes according to some
criterion. Usually the criterion is the estimated mean
squared error or the correlation. The adaptive filters
are time-varying since their parameters are
continually changing in order to meet a performance
requirement. In this sense, an adaptive filter can be
interpreted as a filter that performs the approximation
step on-line. Usually the definition of the
performance criterion requires the existence of a
reference signal that is usually hidden in the
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Figure 2. 2-D adaptive filter for image noise
cancellation.

III.

PROPOSED EXPERIMENTAL
WORK

There are two phases in the proposed
adaptive filter. They are the weight-training phase
and the block-adapting phase. Fig. 3 shows the block
diagram of the proposed adaptive filter.
3.1 Weight-Training Phase (WTP)
In order to improve the convergence rate, a
suitable weight matrix
that will be treated as the
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initial weight matrix
for the processing in the
block-adapting phase is found in the weight-training
phase. In WTP, all the elements of the initial weight
matrix
are set to be zero. That is,
where the element
= 0 for
i = 1,2.., M and j = 1,2,..,N. Then, the TDBLMS
algorithm is applied to process the original noisy
image that will be scanned block-by-block from left
to right and from top to down for updating the weight
matrix of each block iteratively until the termination
criterion is reached [10].

Figure 3: Adaptive filter with weight – training
Mechanism.
The operations can be expressed in the equations
from (3) to (5).
We define the termination criterion as
IBNCR I < P
(6)
where P is the termination parameter and BNCR
stands for the block-noise-cancellation ratio that is
defined as
BNCR= 10log [(
)/
]
(7)
In (7), stands for the power of the reference signal
, and can be expressed as

the term

IV.

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SIMULATION RESULTS

The primary input signal with a dimension
of 256 x 256 in the simulation phase is created by
adding a white-Gaussian noise with zero mean to the
ideal image Baboon with 256 gray-levels in Fig.
4(a).Fig (b) shows the primary input image with a
dimension of 400 x 400 and Fig. 4(c) shows the noisy
primary input image with an SNR of 0 dB. The
convergence factor is 4.5 X 10-7. For the digital
filter, the 4-th order transversal FIR filter is chosen to
convolved the reference image and the filter window
with a dimension of 2 x 2 (M = 2, N = 2). In order to
observe the effect of block size on the performance,
four different block sizes of 4 x 4 (L = 4), 8 x 8 (L =
8), 16 x 16 (L = 16), and 32 x 32 (L = 32) are
simulated. Table 1 lists the performance comparison.
The simulation results indicate that the proposed
adaptive filter achieves a better performance;
however, the performance of the TDBLMS algorithm
is not so good for the first several blocks.

(a)
(b)
(c)
Figure 4 (a), (b) Primary input image Baboon with a
dimension of 256x256 and 400x400. (c) Noisy
primary input image with SNR= 0 dB.

(a)

(b)

(c)

(8)
is the power of the primary input signal
(9)

the term
signal

In (8)-(10),
means of ,

,
, and

is the power of the error
(10)
, and
stand for the
, respectively.

B. Block-Adapting Phase (BAP)
Once the suitable weight matrix
in the
weight training phase is found, this weight matrix is
treated as the initial weight matrix
in the blockadapting phase (BAP). In this phase, the original
noisy image is processed according to the TDBLMS
algorithm [10] again for the noise cancellation.
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(d)
(e)
Figure 5: Output of noisy input image for block size
of (a) 2x2 (b) 4x4 (c) 8x8 (d) 16 x 16 (e) 32 x 32

(f)

(g)

(h)

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[6]

(i)
(j)
Figure 6: Output of primary input image with
dimensions of 400 x 400 for block size of (f) 2x2 (g)
4x4 (h) 8x8 (i) 16 x 16 (j) 32 x 32
TABLE 1 Output PSNR of the 2-D adaptive noise
canceller for noisy image with SNR = 0 db
Block size
(LxL)

PSNR(dB)

2x2

50.2754

Proposed
Adaptive
Filter
63.5161

4x4

53.2134
53.8987
52.1246

65.3567

32 x 32

50.0712

[9]

66.7429

16 x 16

[8]

66.1827

8x8

[7]

63.3042

TDBLMS

V.

[10]

[11]

CONCLUSION

This work proposed an adaptive filter for
two dimensional block processing in image noise
cancellation. The simulation performed on the noisy
image baboon with a dimension of 256 x 256 with an
SNR of 0 dB shows that this approach can achieve
the PSNR's of 63.5161, 66.1827, and 66.7429 for the
block sizes of 2 x 2, 4 x 4, 8 x 8, 16 x 16, 32 x 32
respectively. The proposed method provides better
image. The proposed has been tested on well-known
benchmark images, where their PSNR and visual
results show the superiority of the proposed
technique over the conventional techniques.

[12]

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IEEE Trans. Acoust., Speech, Signal
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equalization
using
adaptive
lattice
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G. A. Clark, S. K. Mitra, and S. R. Parker,
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C. S. Burrus, "Block implementation of
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algorithm", IEEE Trans. Circuits Syst.,vol.
35, pp. 485- 494, May 1988.
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[4]

[5]

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