1532 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 9, NO. 9, SEPTEMBER 2000
Adaptive Wavelet Thresholding for Image Denoising
and Compression
S. Grace Chang, Student Member, IEEE, Bin Yu, Senior Member, IEEE, and Martin Vetterli, Fellow, IEEE
Abstract—The first part of this paper proposes an adaptive,
data-driven threshold for image denoising via wavelet soft-thresh-
olding. The threshold is derived in a Bayesian framework, and the
prior used on the wavelet coefficients is the generalized Gaussian
distribution (GGD) widely used in image processing applications.
The proposed threshold is simple and closed-form, and it is adap-
tive to each subband because it depends on data-driven estimates
of the parameters. Experimental results show that the proposed
method, called BayesShrink, is typically within 5% of the MSE
of the best soft-thresholding benchmark with the image assumed
known. It also outperforms Donoho and Johnstone’s SureShrink
most of the time.
The second part of the paper attempts to further validate
recent claims that lossy compression can be used for denoising.
The BayesShrink threshold can aid in the parameter selection
of a coder designed with the intention of denoising, and thus
achieving simultaneous denoising and compression. Specifically,
the zero-zone in the quantization step of compression is analogous
to the threshold value in the thresholding function. The remaining
coder design parameters are chosen based on a criterion derived
from Rissanen’s minimum description length (MDL) principle.
Experiments show that this compression method does indeed re-
move noise significantly, especially for large noise power. However,
it introduces quantization noise and should be used only if bitrate
were an additional concern to denoising.
Index Terms—Adaptive method, image compression, image de-
noising, image restoration, wavelet thresholding.
I. INTRODUCTION
A
N IMAGE is often corrupted by noise in its acquisition or
transmission. The goal of denoising is to remove the noise
while retaining as much as possible the important signal fea-
tures. Traditionally, this is achieved by linear processing such
as Wiener filtering. A vast literature has emerged recently on
Manuscript received January 22, 1998; revised April 7, 2000. This work
was supported in part by the NSF Graduate Fellowship and the Univer-
sity of California Dissertation Fellowship to S. G. Chang; ARO Grant
DAAH04-94-G-0232 and NSF Grant DMS-9322817 to B. Yu; and NSF Grant
MIP-93-213002 and Swiss NSF Grant 20-52347.97 to M. Vetterli. Part of this
work was presented at the IEEE International Conference on Image Processing,
Santa Barbara, CA, October 1997. The associate editor coordinating the
review of this manuscript and approving it for publication was Prof. Patrick L.
Combettes.
S.G. Chang was with the Department of Electrical Engineering and Computer
Sciences, University of California, Berkeley, CA 94720 USA. She is now with
Hewlett-Packard Company, Grenoble, France (e-mail: grchang@yahoo.com).
B. Yu is with the Department of Statistics, University of California, Berkeley,
CA 94720 USA (e-mail: binyu@stat.berkeley.edu)
M. Vetterli is with the Laboratory of Audiovisual Communications, Swiss
Federal Institute of Technology (EPFL), Lausanne, Switzerland and also with
the Department of Electrical Engineering and Computer Sciences, University of
California, Berkeley, CA 94720 USA.
Publisher Item Identifier S 1057-7149(00)06914-1.
signal denoising using nonlinear techniques, in the setting of
additive white Gaussian noise. The seminal work on signal de-
noising via wavelet thresholding or shrinkage of Donoho and
Johnstone ([13]–[16]) have shown that various wavelet thresh-
olding schemes for denoising have near-optimal properties in
the minimax sense and perform well in simulation studies of
one-dimensional curve estimation. It has been shown to have
better rates of convergence than linear methods for approxi-
mating functions in Besov spaces ([13], [14]). Thresholding is
a nonlinear technique, yet it is very simple because it operates
on one wavelet coefficient at a time. Alternative approaches to
nonlinear wavelet-baseddenoising can be found in, for example,
[1], [4], [8]–[10], [12], [18], [19], [24], [27]–[29], [32], [33],
[35], and references therein.
On a seemingly unrelated front, lossy compression has been
proposed for denoising in several works [6], [5], [21], [25],
[28]. Concerns regarding the compression rate were explicitly
addressed. This is important because any practical coder must
assume a limited resource (such as bits) at its disposal for repre-
senting the data. Other works [4], [12]–[16] also addressed the
connection between compression and denoising, especially with
nonlinear algorithms such as wavelet thresholding in a mathe-
matical framework. However, these latter works were not con-
cerned with quantization and bitrates: compression results from
a reduced number of nonzero wavelet coefficients, and not from
an explicit design of a coder.
The intuition behind using lossy compression for denoising
may be explained as follows. A signal typically has structural
correlations that a good coder can exploit to yield a concise rep-
resentation. White noise, however, does not have structural re-
dundancies and thus is not easily compressable. Hence, a good
compression method can provide a suitable model for distin-
guishing between signal and noise. The discussion will be re-
stricted to wavelet-based coders, though these insights can be
extended to other transform-domain coders as well. A concrete
connection between lossy compression and denoising can easily
be seen when one examines the similarity between thresholding
and quantization, the latter of which is a necessarystep in a prac-
tical lossy coder. That is,the quantization of wavelet coefficients
with a zero-zone is an approximation to the thresholding func-
tion (see Fig. 1). Thus, provided that the quantization outside
of the zero-zone does not introduce significant distortion, it fol-
lows that wavelet-based lossy compression achieves denoising.
With this connection in mind, this paper is about wavelet thresh-
olding for image denoising and also for lossy compression. The
threshold choice aids the lossy coder to choose its zero-zone,
and the resulting coder achieves simultaneous denoising and
compression if such property is desired.
1057–7149/00$10.00 © 2000 IEEE
CHANG et al.: ADAPTIVE WAVELET THRESHOLDING FOR IMAGE DENOISING AND COMPRESSION 1533
Fig. 1. Thresholding function can be approximated by quantization with a
zero-zone.
Thetheoreticalformalization offiltering additiveiid Gaussian
noise (of zero-mean and standard deviation
) via thresholding
wavelet coefficients was pioneered by Donoho and Johnstone
[14]. A wavelet coefficient is compared to a given threshold and
issetto zeroifits magnitudeisless thanthethreshold; otherwise,
it is kept or modified (depending on the thresholding rule). The
threshold acts as an oracle which distinguishes between the
insignificant coefficients likely due to noise, and the significant
coefficients consisting of important signal structures. Thresh-
olding rules are especially effective for signals with sparse or
near-sparserepresentationswhereonlyasmallsubsetofthecoef-
ficients represents all or most of the signal energy. Thresholding
essentially creates a region around zero where the coefficients
are considered negligible.Outside of this region, the thresholded
coefficients are kept to full precision (that is, without quanti-
zation). Their most well-known thresholding methods include
VisuShrink [14] and SureShrink [15]. These threshold choices
enjoy asymptotic minimax optimalities over function spaces
such as Besovspaces.Forimage denoising, however, VisuShrink
is known to yield overly smoothed images. This is because its
thresholdchoice,
(calledtheuniversalthreshold and
is the noise variance), can be unwarrantedly large due to its
dependenceonthenumberofsamples,
,whichismorethan
foratypicaltestimageofsize .SureShrinkusesahybrid
of the universal threshold and the SURE threshold, derivedfrom
minimizing Stein’s unbiased risk estimator [30], and has been
showntoperformwell.SureShrinkwillbethemaincomparisonto
the method proposed here, and,as will be seen later in this paper,
ourproposedthresholdoftenyieldsbetterresult.
Since the works of Donoho and Johnstone, there has been
much research on finding thresholds for nonparametric estima-
tion in statistics. However, few are specifically tailored for im-
ages. In this paper, we propose a framework and a near-op-
timal threshold in this framework more suitable for image de-
noising. This approach can be formally described as Bayesian,
but this only describes our mathematical formulation, not our
philosophy. The formulation is grounded on the empirical ob-
servation that the wavelet coefficients in a subband of a natural
image canbe summarizedadequatelybyageneralized Gaussian
distribution (GGD). This observation is well-accepted in the
image processing community (for example, see [20], [22], [23],
[29], [34], [36]) and is used for state-of-the-art image coders
in [20], [22], [36]. It follows from this observation that the av-
erage MSE (in a subband) can be approximated by the corre-
sponding Bayesian squared error risk with the GGD as the prior
applied to each in an iid fashion. That is, a sum is approximated
by an integral. We emphasize that this is an analytical approx-
imation and our framework is broader than assuming wavelet
coefficients are iid draws from a GGD. The goal is to find the
soft-threshold that minimizes this Bayesian risk, and we call our
method BayesShrink.
The proposed Bayesian risk minimization is subband-depen-
dent. Given the signal being generalized Gaussian distributed
and the noise being Gaussian, via numerical calculation a nearly
optimal threshold for soft-thresholding is found to be
(where is the noise variance and the signal vari-
ance). This threshold gives a risk within 5% of the minimal risk
over a broad range of parameters in the GGD family. To make
this threshold data-driven, the parameters
and are esti-
mated from the observed data, one set for each subband.
To achieve simultaneous denoising and compression, the
nonzero thresholded wavelet coefficients need to be quantized.
Uniform quantizer and centroid reconstruction is used on the
GGD. The design parameters of the coder,such as the number of
quantizationlevelsandbinwidths,aredecidedbasedonacriterion
derived from Rissanen’s minimum description length (MDL)
principle [26]. This criterion balances the tradeoff between the
compression rate and distortion, and yields a nice interpretation
ofoperatingatafixedslopeontherate-distortioncurve.
The paper is organized as follows. In Section II, the wavelet
thresholding idea is introduced. Section II-A explains the
derivation of the BayesShrink threshold by minimizing a
Bayesian risk with squared error. The lossy compression based
on the MDL criterion is explained in Section III. Experimental
results on several test images are shown in Section IV and
compared with SureShrink. To benchmark against the best
possible performance of a threshold estimate, the compar-
isons also include OracleShrink, the best soft-thresholding
estimate obtainable assuming the original image known, and
OracleThresh, the best hard-thresholding counterpart. The
BayesShrink method often comes to within 5% of the MSEs
of OracleShrink, and is better than SureShrink up to 8% most
of the time, or is within 1% if it is worse. Furthermore, the
BayesShrink threshold is very easy to compute. BayesShrink
with the additional MDL-based compression, as expected,
introduces quantization noise to the image. This distortion may
negate the denoising achieved by thresholding, especially when
is small. However, for larger values of , the MSE due to the
lossy compression is still significantly lower than that of the
noisy image, while fewer bits are used to code the image, thus
achieving both denoising and compression.
II. W
AVELET THRESHOLDING AND THRESHOLD SELECTION
Let the signal be , where is some
integer power of 2. It has been corrupted by additive noise and
one observes
(1)
where
are independent and identically distributed ( )
as normal
and independent of . The goal is to
remove the noise, or “denoise”
, and to obtain an estimate
of which minimizes the mean squared error (MSE),
MSE
(2)
1534 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 9, NO. 9, SEPTEMBER 2000
Fig. 2. Subbands of the 2-D orthogonal wavelet transform.
Let , , and ; that is,
the boldfaced letters will denote the matrix representation of the
signals under consideration. Let
denote the matrix
of wavelet coefficients of
, where is the two-dimensional
dyadic orthogonal wavelet transform operator, and similarly
and . The readers are referred to references such as
[23], [31] for details of the two-dimensional orthogonal wavelet
transform. It is convenient to label the subbands of the transform
as in Fig. 2. The subbands
, , ,
are called the details, where is the scale, with being the
largest (or coarsest) scale in the decomposition, and a subband
at scale
has size . The subband is the low
resolution residual, and
is typically chosen large enough such
that
and . Note that since the transform
is orthogonal,
are also iid .
The wavelet-thresholding denoising method filters each co-
efficient
from the detail subbands with a threshold function
(to be explained shortly) to obtain
. The denoised estimate is
then
, where is the inverse wavelet transform
operator.
There are two thresholding methods frequently used. The
soft-threshold function (also called the shrinkage function)
sgn (3)
takes the argument and shrinks it toward zero by the threshold
. The other popular alternative is the hard-threshold function
(4)
which keeps the input if it is larger than the threshold
; oth-
erwise, it is set to zero. The wavelet thresholding procedure re-
moves noise by thresholding only the wavelet coefficients of the
detail subbands, while keeping the low resolution coefficients
unaltered.
The soft-thresholding rule is chosen over hard-thresholding
for several reasons. First, soft-thresholding has been shown to
achieve near-optimal minimax rate over a large range of Besov
spaces [12], [14]. Second, for the generalized Gaussian prior
assumed in this work, the optimal soft-thresholding estimator
yields a smaller risk than the optimal hard-thresholding esti-
mator (to be shown later in this section). Lastly, in practice, the
soft-thresholding method yields more visually pleasant images
over hard-thresholding because the latter is discontinuous and
yields abrupt artifacts in the recovered images, especially when
the noise energy is significant. In what follows, soft-thresh-
olding will be the primary focus.
While the idea of thresholding is simple and effective,
finding a good threshold is not an easy task. For one-dimen-
sional (1-D) deterministic signal of length
, Donoho and
Johnstone [14] proposed for VisuShrink the universal threshold,
, which results in an estimate asymptotically
optimal in the minimax sense (minimizing the maximum
error over all possible
-sample signals). One other notable
threshold is the SURE threshold [15], derived from minimizing
Stein’s unbiased risk estimate [30] when soft-thresholding
is used. The SureShrink method is a hybrid of the universal
and the SURE threshold, with the choice being dependent
on the energy of the particular subband [15]. The SURE
threshold is data-driven, does not depend on
explicitly,
and SureShrink estimates it in a subband-adaptive manner.
Moreover, SureShrink has yielded good image denoising
performance and comes close to the true minimum MSE of the
optimal soft-threshold estimator (cf. [4], [12]), and thus will be
the main comparison to our proposed method.
In the statistical Bayesian literature, many works have con-
centrated on deriving the best threshold (or shrinkage factor)
based on priors such as the Laplacian and a mixture of Gaus-
sians (cf. [1], [8], [9], [18], [24], [27], [29], [32], [35]). With an
integral approximation to the pixel-wise MSE distortion mea-
sure as discussed earlier, the formulation here is also Bayesian
for finding the best soft-thresholding rule under the general-
ized Gaussian prior. A related work is [27] where the hard-
thresholding rule is investigated for signals with Laplacian and
Gaussian distributions.
The GGD has been used in many subband or wavelet-based
image processing applications [2], [20], [22], [23], [29], [34],
[36]. In [29], it was observed that a GGD with the shape param-
eter
ranging from 0.5 to 1 [see (1)] can adequately describe the
wavelet coefficients of a large set of natural images. Our expe-
rience with images supports the same conclusion. Fig. 3 shows
the histogram of the wavelet coefficients of the images shown in
Fig. 9, against the generalized Gaussian curve, with the param-
eters labeled (the estimation of the parameters will be explained
later in the text.) A heuristic can be set forward to explain why
there are a large number of “small” coefficients but relatively
few “large” coefficients as the GGD suggests: the small ones
correspond to smooth regions in a natural image and the large
ones to edges or textures.
A. Adaptive Threshold for BayesShrink
The GGD, following [20], is
(5)
, , , where
and
CHANG et al.: ADAPTIVE WAVELET THRESHOLDING FOR IMAGE DENOISING AND COMPRESSION 1535
(a) (b)
(c) (d)
Fig. 3. Histogram of the wavelet coefficients of four test images. For each image, from top to bottom it is fine to coarse scales: from left to right, they are the
HH
,
HL
, and
LH
subbands, respectively.
and is the gamma function. The pa-
rameter
is the standard deviation and is the shape pa-
rameter. For a given set of parameters, the objective is to find
a soft-threshold
which minimizes the Bayes risk,
(6)
where
, and .
Denote the optimal threshold by
,
(7)
which is a function of the parameters
and . To our knowl-
edge, there is no closed form solution for
for this chosen
prior, thus numerical calculation is used to find its value.
1
Before examining the general case, it is insightful to consider
two special cases of the GGD: the Gaussian (
) and the
1
It was observed that for the numerical calculation, it is more robust to obtain
the value of
T
from locating the zero-crossing of the derivative,
r
(
T
)
, than
from minimizing
r
(
T
)
directly. In the Laplacian case, a recent work [17] derives
an analytical equation for
T
to satisfy and calculates
T
by solving such an
equation.
Laplacian ( ) distributions. The Laplacian case is particu-
larly interesting, because it is analytically more tractable and is
often used in image processing applications.
Case 1: (Gaussian)
with .Itis
straightforward to verify that
(8)
(9)
where
(10)
with the standard normal density function
and the survival function of the
standard normal
.
1536 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 9, NO. 9, SEPTEMBER 2000
(a)
(b)
Fig. 4. Thresholding for the Gaussian prior, with
=1
. (a) Compare the
optimal threshold
T
(
;
2)
(solid —) and the threshold
T
(
)
(dotted
111
) against the standard deviation
on the horizontal axis. (b) Compare the
risk of using optimal soft-thresholding (—),
T
for soft-thresholding (
111
), and
optimal hard-thresholding (- - -).
Assuming for the time being, the value of
is found numerically for different values of and is plotted
against
in Fig. 4(a), with
(11)
superimposed on top. It is clear that this simple and closed-form
expression,
, is very close to the numerically found
. The expected risks of and are
shown in Fig. 4(b) for
, where the maximum deviation of
is less than 1% of the optimal risk, .
For general
, it is an easy scaling exercise to see that (11)
becomes
(12)
For a further comparison, the risk for hard-thresholding is
also calculated. After some algebra, it can be shown that the
risk for hard-thresholding is
(13)
By setting to zero the derivative of (13) with respect to
, the
optimal threshold is found to be
if
if
anything if
(14)
with the associated risk
if
if
(15)
Fig. 4(b) shows that both the optimal and near-optimal soft-
threshold estimators,
and , achieve lower risks
than the optimal hard-threshold estimator.
The threshold
is not only nearly optimal but
also has an intuitive appeal. The normalized threshold,
,
is inversely proportional to
, the standard deviation of , and
proportional to
, the noise standard deviation. When
, the signal is much stronger than the noise, is chosen
to be small in order to preserve most of the signal and remove
some of the noise; vice versa, when
, the noise dom-
inates and the normalized threshold is chosen to be large to re-
move the noise which has overwhelmed the signal. Thus, this
threshold choice adapts to both the signal and noise character-
istics as reflected in the parameters
and .
Case 2: (Laplacian) With
, the GGD becomes Lapla-
cian:
LAP . Again
for the time being let
. The optimal threshold
found numerically is plotted against the standard deviation
on the horizontal axis in Fig. 5(a). The curve of (in
solid line —) is compared with
(in dotted line
) in Fig. 5(a). Their corresponding expected risks are shown
in Fig. 5(b), and the deviation of
from the is
less than 0.8%. This suggests that
also works well in
the Laplacian case. For general
, (12) holds again.
The threshold choice
was found in-
dependently in [27] for approximating the optimal hard-thresh-
olding using the Laplacian prior. Fig. 5(a) compares the optimal
hard-threshold,
, and to the soft-thresholds
and . The corresponding risks are plotted
in Fig. 5(b), which shows the soft-thresholding rule to yield a
lower risk for this chosen prior. In fact, for
larger than ap-
proximately 1.3
, the risk of the approximate hard-threshold is
worse than if no thresholding were performed (which yields a
risk of
).
When
tends to infinity or the SNR is going to infinity,
an asymptotic approximation of
is derived in [17] in
this Laplacian case to be
. However, in the same article,
this asymptotic approximation is outperformed in seven test im-
ages by the our proposed threshold,
.
