Mission-critical monitoring based on surround suppression variational Retinex enhancement for non-uniform illumination images
Rao et al. EURASIP Journal on Wireless Communications
and Networking
Mission-critical monitoring based on surround suppression variational Retinex enhancement for non-uniform illumination images
Zhitao Rao 0
Tingfa Xu 0
Hongqing Wang 0
0 School of Optoelectronics, Image Engineering andVideo Technology Lab, Beijing Institute of Technology , Beijing 100081 , China
In this letter, a surround suppression variational Retinex enhancement algorithm (SSVR) is proposed for non-uniform illumination images. Instead of a gradient module, a surround suppression mechanism is used to provide spatial information in order to constrain the total variation regularization strength of the illumination and reflectance. The proposed strategy preserves the boundary areas in the illumination so that halo artifacts are prevented. It also preserves textural details in the reflectance to prevent from illumination compression, which further contributes to the contrast enhancement in the resulting image. In addition, strong regularization strength is enforced to eliminate uneven intensities in the homogeneous areas. The split Bregman optimization algorithm is employed to solve the proposed model. Finally, after decomposition, a contrast gain is added to reflectance for contrast enhancement, and a Laplacianbased gamma correction is added to illumination for prevent color cast. The recombination of the modified reflectance and illumination become the final result. Experimental results demonstrate that the proposed SSVR algorithm performs better than other methods.
Image enhancement; Surround suppression variational Retinex; Non-uniform illumination images; Split Bregman optimization; Contrast gain; Laplacian-based gamma correction
1 Introduction
Image enhancement techniques have been widely used
in various applications in the past few decades, including
face recognition [1, 2], micro-imaging [3] and intelligent
video surveillance system [4] etc.. The primary purpose
of image enhancement is to improve the contrast or
perception of an image without losing details or introducing
novel artifacts. In generally, many classical enhancement
methods have been proposed, including sigmoid based
algorithms [5], logarithmic domain algorithms [6],
histogram equalization (HE) algorithms [7, 8], unsharp
masking algorithms [9], and Retinex algorithms [10]. Sigmoid
and logarithmic based methods are simple and effective
for global brightness and contrast enhancement, but
spatial information of images are not considered. HE
algorithm is simple and widely used. But it is limited for
the uneven illumination images, especially for dark areas.
For unsharp masking algorithms, images are decomposed
into high-frequency and low-frequency terms, which are
processed respectively. Result images by this method well
preserve details, but introduce unnatural looking.
Amongst the various enhancement methods, Retinex
has received much attention due to its simplicity and
effectiveness in enhancing non-uniform illumination
images [11]. Land and McCann first proposed Retinex
algorithm, which is a model of color and luminance
perception of human visual system (HVS). To simulate the
mechanism of HVS, it is an ill-posed problem that
computes illumination or reflectance from a single observed
image. To overcome this problem, many modified Retinex
theories have been proposed. Retinex algorithms are
basically categorized into path-based methods [12–14], center-/
surround-based methods [15–18], recursive methods
[19–21], PDE-based methods [22–24], and variational
methods [11, 25–30]. Path-based Retinex methods are
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the simplest, but they usually necessitate high
computational complexity. For the center-/surround-based methods,
Gaussian filtering is usually used as a low pass filter to
estimate the illumination. In order to get better performance
for RGB images, Jobson et al. had put forward multi-scale
Retinex (MSR) [16, 17] algorithm and color restored
multiscale Retinex (CRMSR) [18] algorithm. These algorithms
are easy to implement but too many parameters need to be
manually set. Large numbers of iterations lower recursive
methods’ efficiency. In 1974, Horn introduced partial
differential equation (PDE) as a novel mathematical model to the
Retinex algorithm [22]. PDE-based methods are usually
based on the assumption that the illumination varies
smoothly, while the reflectance changes at sharp edges. So,
reflectance component can be estimated by solving
Poisson equation. In 2010, Morel proposed a new
PDEbased Retinex method that computed the divergence by
thresholding the components of the gradient prior
instead of the scalar Laplacian operator [23]. However,
the hard thresholding operator in PDE-based Retinex
will cause extra artifacts when solving the Poisson
equations. In [24], Zosso presented a unifying
framework for Retinex in which existing Retinex algorithms
can be represented within a single model. He defined
Retinex model in more general two steps: first, looking
for a filtered gradient to solve the optimazation
problem consisting of sparsity prior and quadratic fidelity
prior of the reflectance; second, finding a reflectance
whose actual gradient comes close. Based on the same
assumption of PDE, a variational framework for the
Retinex algorithm has been proposed. In 2003, Kimmel
indicated that the illumination estimation problem can be
formulated as a quadratic programming optimization
problem [25]. M. Ng et al. [26] proposed a total variational
model for Retinex in which both illumination and
reflectance components are considered. X. Lan et al. [28]
introduced the concept of spatial information for the uneven
intensity correction. Different regularization strength of
the reflectance is enforced to get more accurate results for
non-uniform illumination images. And the split Bregman
algorithm is employed to solve the proposed adaptive
Retinex variational model. In 2014, L. Wang et al. [11]
proposed variational bayesian model for Retinex by
combining the variational Retinex and Beyesian theory. Due to
the shortage of the traditional variational method on
limiting the scope of reflectance and illumination
components, Wei Wang [30] proposed a variational model with
barrier functionals for Retinex. They built a new energy
function by adding two barriers for getting a better output.
In this paper, a novel image enhancement algorithm for
non-uniform illumination images is proposed. First, the
variational Retinex model estimates the reflectance and
illumination components simultaneously. A surround
suppression mechanism, which is a human visual property, is
used to constrain the TV regularization strength of both
reflectance and illumination. Moreover, the Split Bregman
algorithm is used to solve the proposed variational
model. Second, after decomposition, a contrast gain is
added to reflectance for contrast enhancement, and a
Laplacian-based gamma correction is added to illumination
for prevent color cast. The recombination of the modified
reflectance and illumination become the final result.
The remainder of this paper is organized as follows.
The Retinex theory and the proposed SSVR algorithm
are introduced in Section 2. Experimental results and
comparison of SSVR with other methods are devoted in
Section 3. Finally, Section 4 concludes the paper.
2 The proposed algorithm
2.1 Retinex theory
Recently, more and more attention has been paid to
color image processing. However, general enhancement
algorithms process the image in greyscale values that do
not consider the color information. Retinex methods
have been proposed for color images based on human
visual system (HVS).
Retinex theory was first proposed by Land and McCann
in 1971 [10]. According to Retinex model, a given image
can be decomposed into two parts: the illumination and
the reflectance components. It is defined as follows:
For easy calculation, equation (1) is usually converted
into the logarithmic form, as shown:
where s = log(S), l = log(L), r = log(R). The Retinex obtains
the reflectance component channel by channel [6].
2.2 A spatially adaptive retinex variational model
Amongst the various Retinex-based contrast
enhancement methods, variational Retinex model has received
much attention due to its effectiveness in enhancing
images with non-uniform lighting. Li et al. [29] proposed
a perceptually inspired variational model to directly
restore the reflectance and to adjust the uneven intensity
distribution in remote sensing images. In this work, the
relationship and the fidelity term between the
illumination and reflectance are not considered. Besides, Li et
al. [29] divided the spatial domain into edges and
nonedges in the regularization term, which is a kind of hard
segmentation. To overcome these problems, Lan X et al.
[28] proposed a spatially adaptive Retinex variational
model in which a weighted gradient parameter controls
the TV regularization strength of the reflectance. This
can be shown as follows:
is basically divided into two areas: an annular inhibitory
surround called NCRF (non-classical receptive field) and a
central region called CRF (classical receptive field). The
stimulus sensed by NCRF always weakens the CRF’s
response to the stimulus in the central region. In Fig. 1b, the
sensations of the central points in region A, B, and C are
suppressed by their surroundings [31].The effect is strong
in texture region C because its surrounding is full of
textures, whereas the boundary regions A and B are less
affected. Thus, the boundaries of A and B are highlighted.
The effects of surround suppression are shown in Fig. 2.
Herein, we extract some critical steps of the method in
the literature [32] to suppress the texture gradients for
our variational Retinex model. The extracted suppression
procedure for the gradient module |∇ s (x, y)| of an image
is expressed as follows:
Then, the suppression term t (x, y) for each pixel is
calculated by convolving the gradient module with
the weighting function ωσ(x, y):
tðx; yÞ ¼ j∇sðx; yÞj
minEðr; lÞ ¼ X ks−l−rk2 þ αk∇lk2
Ω
þμwk∇rk þ β½expðrÞ−0:5 2 s:t: l≥s; r≤0;
ð3Þ
where α, μ, β is parameters that control each item in this
model, w is a weight parameter that controls the TV
regularization strength of the reflectance component, it is
defined as equation (4). In equation (4), K is the threshold
that differentiates the boundary edges from the suppressed
texture edges. In this paper, K is set to be equal to the 90%
value of the cumulative distribution function.
However, directly applying equation (3–4) cannot
effectively obtain the expected reflectance and
illumination. The reason for this is that the criterion for
selective smoothing depends on the gradient module, which is
unable to fully demarcate between texture edges and
boundary edges in real scenes. Some of the textures
could have higher gradients than some boundaries and,
hence, weaker diffusivities. In this paper, we propose a
novel adaptive Retinex variational model. Instead of the
gradient module, a surround suppression mechanism,
which is a human visual property, is introduced to
achieve this goal. The proposed strategy preserves the
boundary areas in the illumination so that halo artifacts
are prevented. It also preserves textural details in the
reflectance to prevent from illumination compression,
which further contributes to the contrast enhancement
in the resulting image. In addition, strong regularization
strength is enforced to eliminate uneven intensities in
the homogeneous areas. The split Bregman optimization
algorithm was employed to solve the proposed model.
2.3 Surround suppression mechanism
When recognizing objects by judging their contours, the
human visual system has a surround suppression
mechanism to suppress textural information. This mechanism
and its effects are shown in Fig. 1. As shown in Fig. 1a, it
Fig. 1 Surround Suppression. a Model of surround suppression. b Suppression effects in different areas
1 x2 þ y2! 1 x2 þ y2
DoGσðx; yÞ ¼ 2πð4σÞ2 exp − 2ð4σÞ2 − 2πσ2 exp − 2σ2
Fig. 2 Effects of Surround Suppression. a Original image. b Gradient module of a. c Boundary template
Finally, we define the suppressed gradient value B(x, y)
and T(x, y) as follows:
control the TV regularization strength of the reflectance
and illumination, and are defined as follows:
T ðx; yÞ ¼ Uðtðx; yÞÞ;
In equations (9–10), suppression term t(x, y) is small
for the boundary edges and large for the texture edges.
Here, αt is the suppression strength factor, which directly
influences the suppression effect. B(x, y) can successfully
assign high values in the boundary areas, T(x, y)
assigns high values in the texture areas. So, B(x, y) and
T(x, y) are regarded as boundary and texture
templates, respectively.
2.4 Surround suppression variational retinex model
TV regularization strength of both illumination and
reflectance should be associated with the spatial information
of the surround suppression mechanism. This strategy
preserves the boundary areas in the illumination so that
the halo artifacts are prevented. It preserves the textural
details in the reflectance to prevent illumination
compression, which contributes to the contrast enhancement in
the result. Besides, strong regularization strength is
enforced to eliminate the uneven intensity in the
homogeneous areas. The proposed adaptive surround suppression
based variational Retinex model can be shown as follows:
minEðr; lÞ ¼ X ks−l−rk2 þ μwk∇rk þ ανk∇lk
where α, μ, and β in equation (11) are the same as in
equation (3), w and v are weight parameters, which
Here, θ(x) is a monotone decreasing function. We
refer to the analysis in [33] and define the modified
function:
where K is the threshold that differentiates the boundary
edges from the suppressed texture edges. In this paper,
K is set to be equal to the 90% value of the cumulative
distribution function of Bk(x, y) or Tk(x, y).
2.5 Split Bregman algorithm for the proposed model
Since two unknown variables exist in equation (11), an
alternating minimization scheme is used to minimize the
cost function (11). The minimization problem (11) is
converted into two subproblems as follows:
mrin E1ðrÞ ¼ XΩ ks−l−rk2 þ μwk∇rk þ β½expðrÞ−0:5 2 ;
mlin E2ðlÞ ¼ X ks−l−rk2 þ ανk∇lk;
The split Bregman algorithm [26, 34] is a very
efficient way to solve the minimization subproblem in
(14) and (15). By introducing a new variable, the
subproblem (14) is converted into the following
constrained problem:
minr;d E1ðrÞ ¼ X ks−l−rk2 þ μωkdk
In order to solve the constrained problem, an L2 penalty
term is added to get an unconstrained problem:
where λ is a nonnegative parameter, and b1 is the Bregman
parameter. The computation procedure is detailed in
Algorithm 1.
Secondly, update uj + 1 by minimizing the differentiable
optimization problem in the following:
3 Algorithm 1
Step 1: Initialize u0 ¼ 0; j ¼ 0; and b01 ¼ b01h; b10v ¼ 0;
where “h” and “v” stands for the horizontal axis and the
vertical axis, respectively.
Step 2: Firstly, given uj and bj1, update dj + 1 as follows:
djþ1 ¼ shrinkage ∇uj þ bj1; μ2wλ ;
where shrinkage is the soft shrinkage operator, defined as
Fig. 3 Experimental results on church. a Original image; b MSR; c LHE; d AL; e ALTM; f GUM; g SARV; h NPE; i SSVR
s−li−u 2 þ β½expðuÞ−0:5 2 þ λ djþ1−∇u−bj1 2 );
which can be solved by the Fourier transform and
Gauss-Seidel method, etc. Here, we use the Fourier
transform to solve it by
ujþ1 ¼ F−1( F s−li −βðFðexpðuj−0:5ÞÞÞ þ λP )
where F is Fourier transformation, F–1 is the inverse
Fourier transformation, and F* is conjugated Fourier
transformation. P is denoted as
P ¼ F ð∇hÞF dhjþ1−bj1h þ F ð∇vÞF dvjþ1−bj1v ;
Thirdly, update as follows:
b1jþ1 ¼ bj1− djþ1−∇ujþ1 ;
Step 3: If (‖uj + 1 − uj‖/‖uj + 1‖) ≤ εu, ri + 1/2 = uj + 1, ri + 1 =
min(ri + 1/2, 0); otherwise, go to Step 2.
The solution of minimizing subproblem in (15) is
same as the solution of minimizing subproblem in (14).
By introducing a new variable, the subproblem (15) is
converted into the following constrained problem:
In order to solve the constrained problem, an L2
penalty term is added to get an unconstrained problem:
where λ is a nonnegative parameter, and b2 is the Bregman
parameter. The computation procedure is detailed in
Algorithm 2.
4 Algorithm 2
Fig. 4 Experimental results on beach. a Original image; b MSR; c LHE; d AL; e ALTM; f GUM; g SARV; h NPE; i SSVR
Step 2: Firstly, given wj and bj2, update tj + 1 as follows:
tjþ1 ¼ shrinkage ∇wj þ bj2; 2αγν ;
where shrinkage is the soft shrinkage operator, defined as
Secondly, update wj + 1 by minimizing the differentiable
optimization problem in the following:
s−riþ1−w 2 þ γ tjþ1−∇w−bj2 2 );
Here, we use the Fourier transform to solve it by
wjþ1 ¼ F−1
where F is Fourier transformation, F-1 is the inverse
Thirdly, update as follows:
b2jþ1 ¼ bj2− t jþ1−∇w jþ1 ;
Fourier transformation, and F* is conjugated Fourier
transformation. Q is denoted as
Q ¼ F ð∇hÞF thjþ1−bj2h þ F ð∇vÞF tvjþ1−bj2v ;
Step 3: If (‖wj + 1 − wj‖/‖wj + 1‖) ≤ εw, li + 1/2 = wj + 1, li + 1 =
max(li + 1/2, s); otherwise, go to Step 2.
Finally, we give the overall procedure for solving the
proposed model in the following:
1. Given that the input image s, initialize l0 = s. For i =
0, 1, 2,……
2. Given li, solve the subproblem (14) to get ri + 1/2 by using Algorithm 1. Then, update ri + 1 by ri + 1 = min(ri + 1/2, 0)
Given ri + 1, solve the subproblem (15) to get li + 1/2.
Then, update li + 1 by li + 1 = max(li + 1/2, s).
Fig. 5 Experimental results on girl. a Original image; b MSR; c LHE; d AL; e ALTM; f GUM; g SARV; h NPE; i SSVR
3. Go back to (2) until (‖rj + 1 − rj‖/‖rj + 1‖) ≤ εr and
(‖lj + 1 − lj‖/‖lj + 1‖) ≤ εl are satisfied.
4.1 Contrast gain and gamma correction
Most Retinex based enhancement algorithms estimate
the reflectance component as the final result. However,
reflectance should be within [0~1], which means that it
cannot completely contains the whole information of
input image. Moreover, illumination component
represents ambience information [35, 36].
In order to preserve the naturalness as well as enhance
details, we add a contrast gain for reflectance and a
gamma correction operation for illumination after the
decomposition. These two steps are processed channel
by channel. Contrast gain is defined as follows:
In this step, input image is first divided into
nonoverlapping 12*12 sub-blocks. σ(x, y) is corresponding
R~ðx; yÞ ¼ Rðx; yÞ=Rmax
variance within current sub-block, σmax is maximum
variance of all sub-blocks. Rmax is maximum pixel value.
λ is an adjusted parameter which is set 0.1 empirically.
Due to lighting geometry and illuminant color, images
captured by cameras may introduce color cast. We
proposed an assumption that color variance is Laplacian-based
distributed. So, gamma correction is defined as follows:
L γ
W
ði; jÞ∈S
Dði; jÞ ¼ jLmaxði; jÞ−Lði; jÞj
where W is the white value (it is equal to 255 in an 8-bit
image), (i, j) is corresponding illumination location in
region S, S represents a region which is selected from
the top 0.1% brightest values in its dark channel, D (i, j) is
the color difference. max and min represent maximum
Fig. 6 Experimental results on passage. a Original image; b MSR; c LHE; d AL; e ALTM; f GUM; g SARV; h NPE; i SSVR
and minimum values, μ and b are the location and scale
parameters of Laplacian distribution. Here and the final
result is then given as follows:
5 Experimental results and evaluation
5.1 Subjective assessment
In our experiments, a large number of images were
tested. Due to space limitations, we have only shown
some of the test images in Figs. 3, 4, 5, 6, 7, and 8.
Moreover, the experimental results were calculated using
MATLAB R2011a under Windows 7. The parameters
were set as α = 4, β = 0.06, μ = 0.04, λ = 0.02, and γ = 0.02.
In this paper, the proposed algorithm is compared to the
existing MSR [13], LHE [7], AL [6], ALTM [37], GUM [9],
SARV [28], and NPE [36] methods. Clearly, MSR, LHE,
AL, and GUM gave over enhanced images, simultaneously
saturating the resulting images much further and causing
color distortion. In addition, MSR and AL introduced
serious color distortion. GUM caused serious halo artifacts
on the boundary edges, especially for passage.bmp and
settingsun.bmp. SARV method produced unnatural looking
Fig. 7 Experimental results on bird. a Original image; b MSR; c LHE; d AL; e ALTM; f GUM; g SARV; h NPE; i SSVR
Fig. 8 Experimental results on settingsun. a Original image; b MSR; c LHE; d AL; e ALTM; f GUM; g SARV; h NPE; i SSVR
results with some of the shadows having higher brightness
values than some of the naturally brighter areas. And block
artifacts are apparent on some texture edges. Compared
with the above methods, NPE and SSVR give very
naturallooking images. The enhanced image reveals a lot of details
in the background regions as well as other interesting areas.
5.2 Objective assessment
Tables 1, 2, and 3 show quantitative comparisons on the
six test images. In this paper, the average values of
contrast values, discrete entropy values and COE values by
each method are considered as the parameter of objective
assessment. From Table 1, the proposed SSVR gets the
Table 1 Contrast of compared methods
Red color data represents the highest value, green color data represents the second highest value, blue color data represents the third highest value
Table 2 Discrete entropy of compared methods
Red color data represents the highest value, green color data represents the second highest value, blue color data represents the third highest value
highest contrast. It means that proposed SSVR
obtains the highest visibility level from the original
images. The discrete entropy indicates the ability of
extracting information from an image. In Table 2, the
entropy value of LHE and GUM outperform other
algorithms. NPE has the lowest entropy values among
the five algorithms, in accordance with its
performance on detail enhancement. However, higher entropy
values do not always get better enhanced
performance, both objective assessment and subjective
assessment should be concomitantly considered [38, 39].
Observer intuitive feelings are the most direct method
to evaluate the image quality. In order to get a more
comprehensive evaluation of the quality,we defined a
new evaluation index named color-order-error (COE),
which is used to measure the level of color constancy
preservation. It is defined as:
( σta 2
σa
s
σtb 2)1=2
σb
s
where σ is standard deviation, t and s are target image
and source image, a and b are channels of CIELab color
Table 3 COE values of compared methods
space. In this paper, the original images are source
images, the enhanced images by different methods are
target images. From the definition of COE, we can see
that the smaller the COE value is, the better the color
order is preserved. Table 3 shows our algorithm can
most successfully preserve the color constancy.
6 Conclusions
This paper proposes a surround suppression variational
Retinex enhancement algorithm for image
enhancement of non-uniform illumination images, which not
only enhances the contrast of the image but also
preserves the color constancy. Surround suppression
mechanism, which performs well in accordance with
constraining the TV regularization strength of the
reflectance and illumination. Moreover, in order to
prevent light flickering caused by varying apparently
scenes, a Laplacian-based gamma correction is
conducted on the estimated illumination, which
contributes to the color constancy preservation in the
output image result. Experimental results demonstrate
that the proposed algorithm is better than the existing
algorithms.
Funding
This work was supported by the Major Science Instrument Program of the
National Natural Science Foundation of China under Grant 61527802, the
General Program of National Nature Science Foundationof China under
Grants 61371132, and 61471043, and the International S&T Cooperation
Program of China under Grants 2014DFR10960.
Authors’ contributions
ZR and TX came up with the algorithm and improved the algorithm. In
addition, ZR wrote and revised the paper. HW implemented the algorithm
of LHE, AL, and ALTM for image enhancement and recorded the data. All
authors read and approved the final manuscript.
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