SUGISAKAMASANORI

Journal of Advances in Artificial Life Robotics
Vol. 1(3); December (2020), pp. 122–127
ON LINE ISSN 2435-8061; ISSN-L 2435-8061
https://alife-robotics.org/jallr.html

Research Article

Specular Reflection Analysis and Illumination Manipulation of
Still Images with Single Multicolor Object
Hsuan T. Chang, Wei-Hsiu Chang

Photonics and Information Laboratory, Department of Electrical Engineering, National Yunlin University of Science and Technology, 123, Section
3,University Road, Douliu, Yunlin 64002, Taiwan

ARTICLE INFO
Article History
Received 11 November 2019
Accepted 10 August 2020
Keywords
Chromaticity
Dichromatic-based modelSpecular removal
Color consistence
Virtual illumination

ABSTRACTAn illumination manipulation scheme for a single object in still images is proposed in this paper.
By using the dichromatic-based model, the reflection component can be resolved through a singleimage specular reflection removal method with the characteristics of color constancy. Then,virtual illumination effect can be made through the recombination with the reflection component.
The scenery illumination can be estimated using the proposed automatic method without knowing
the illumination spectra, three-dimensional object modeling, or texture databases. Experimental
results show that the method is useful for handling the images with a single multicolor object inscenes.

© 2022 The Author. Published by Sugisaka Masanori at ALife Robotics Corporation Ltd.
This is an open access article distributed under the CC BY-NC 4.0 license(http://creativecommons.org/licenses/by-nc/4.0/).

In this method, the reflected specular and diffused colors
are viewed as orthogonal color vectors. However, in thebright image textures, the linear distribution model ishardly used to estimate the vectors in the color space.
The specular-free image (SFI) method determines the
diffused components by estimating either the intensity or
chromaticity of the image. Tan’s method [4] generates
the SFI by analyzing the maximum chromaticity space atfirst. In this method, the specular reflection is consideredas the chromaticity deviation and the maximum
chromaticity is set as the pixel common chromaticity.
When the maximum chromaticity is extracted anddiscarded, the specular component can be removed.
However, in a non-single color image, different
maximum chromaticity in the chromaticity space fordifferent textures could be observed. Therefore, thecontroversy exists on selecting the chromaticity. Yoon’s
specular-free two-band image (SFTBI) method [5],which uses the specular-invariant to determine thediffused reflection component, is thus proposed. A
diffused reflection image with low chromaticity can beobtained. In addition, Shen proposed the modified
specular-free image (MSFI) [6] method to improve
Yoon’s method.

1. Introduction
Establishing the relighting in the computer graphics
needs the provision of the light sources from different
directions. Using only one single input image is notenough to complete the relighting of the objects in theimage. In this paper, we propose a method which canmodify the illumination conditions in images. By
separating the specular reflection and automaticallyevaluating the illumination in the image, thereconstruction of 3-D scene can be discarded and we canperform realistic control on illumination colors in images.
In recent research about specular highlight removal [1],most methods on separating the specular reflection
components in a single image are based on the reflection
model established by Shafer [2]. The reflection model
assumes the complex light paths as a simple reflection
component and analyzes the highlight information basedon polarized photography. Thereafter, more related
methods have been proposed. For example, the T-shape
image color space proposed by Shafer and Klinker can beused to analyze the neighboring pixels of an object [3].

Corresponding author’s E-mail: htchang@yuntech.edu.tw, stire001@hotmail.com

122

The SFI method is usually used together with theneighboring region analysis. Although the SFI method
usually cannot get precise component analysis during theseparation process, SFI can easily obtain the result
without specular components. The specular reflection
pixels can be found by analyzing the difference between
the SFI and the intrinsic image in the neighboring region.
In Tan’s method [4], the iteration process is used at low
chromaticity range for both the SFI and normalized
image. The high-quality or bright-color texture images
can be successfully processed by setting regional
chromaticity. Yoon and Shen proposed different
mechanisms to improve the iteration efficiency. Other
types of methods are based on the dichromatic reflection
model. For example, Ping’s inpainting method [7], which
repairs the highlight points (also considered as region ofinterest, ROI). Rouf proposed the star filter to filter thehighlight points based on the structural characteristics [8].
Tan et. al. also proposed a single integrated method toestimate illumination chromaticity from single-coloredand multicolored surfaces [9]. Different from theconventional dichromatic-based methods, the proposed
method deals only with rough highlight regions does notsegment the colors inside them.
Recently, more studies have been proposed andapplied to various applications. Akashi and Okatani
proposed a novel method [10], which can simultaneously
perform the estimation of object colors and the separationof specular components based on a modified version ofsparse non-negative matrix factorization. Wang et. al.
proposed a novel and accurate approach to remove thespecular effect and improve image quality by using thelight field camera [11]. Suo et. al. proposed a method to
remove the specular highlight from a single image and to
especially target for wide applicability to the large varietyof nature scene [12]. To overcome the degradation
problem in texture regions and the blocky effects inseparating the specular reflection, Ren et. al. proposed amethod in which the global color-lines constraint fromthe dichromatic reflection model is derived to recover
specular and diffused components for texture images [13].
In order to remove the specular highlight reflections infacial images that may contain varying illumination
colors, Lin et. al. proposed a method especially forhandling partially saturated pixels, varying illumination
colors, and ambiguities caused by the similarity between
illumination chromaticity and diffuse chromaticity [14].
Guo et. al. proposed a sparse and low-rank reflection
model to avoid the hue-saturation ambiguity in retrieving
an SFI and to preserve scene details in images as muchas possible.

In this paper, the proposed method mainly deals withthe color constancy based on the chromaticity andintegrated the methods of high-light removal proposed by
Tan and Shan [4], [6] to achieve the reflection removal
purpose. By providing the new RGB values inchromaticity, we can arbitrarily set the virtual
illumination onto the object in the image. Moreover, thespecular reflectivity of the object can be arbitrarily
increased or decreased.

Input
image

Illumination
chromaticity

Color
constancy

Output SFI

Normalized
image

Image
normalization

Separating diffusedand specular reflection
components

Diffused and specularreflection components

Set
illumination

Specular-free
image

Chromaticity
iteration

Fig. 1: The system diagram of the proposed method.

2. Proposed Method
Figure 1 shows the system diagram of the proposed
method. The proposed illumination estimation method isbased on the inverse-intensity chromaticity space [9]. In
this space, the specular component will be labeled andthen be transferred to the Hough space to statisticallydetermine the corresponding chromaticity direction.
Illumination estimation mainly aims to provide thecorrect intensity information during the separation ofreflection components. The intensities of non-white lightilluminations are inhomogeneous while changing theintensity. Therefore, we assume that the illuminationtransformation is correctly estimated using the same
compensation mechanism.
The dichromatic-based model is used to determine thediffused reflection components. The SFI can be obtainedusing Tan’s method [9]. Then the improved SFI, called
the simplified mechanism of SFI can achieved. First,
Tan’s method mentioned that the image intensity can bedivided into two parts according to the dichromatic-basedmodel. That is,𝐈𝐈�𝐱𝐱� = 𝑤𝑤d ∫𝛺𝛺 𝑆𝑆(𝜆𝜆)𝐸𝐸(𝜆𝜆)𝐪𝐪(𝜆𝜆)𝑑𝑑λ + 𝑤𝑤S ∫𝛺𝛺 𝐸𝐸(𝜆𝜆)𝐪𝐪(𝜆𝜆)𝑑𝑑λ, (1)where 𝐈𝐈(𝐱𝐱) = {Ir, Ig, Ib} denotes the color vector of image
intensity recorded by a camera. x={x,y} denotes the 2D
coordinates, 𝐪𝐪 = {𝑞𝑞r,𝑞𝑞g,𝑞𝑞b} denotes the 3D vector of
sensor sensitivity. 𝑆𝑆(𝜆𝜆) and 𝐸𝐸(𝜆𝜆) denote the diffused andthe illumination spectral distributions, respectively. wd
and wS denote for the weighting factors of specular anddiffuse reflection, respectively. The values depend on thegeometric structure in the regional coordinate 𝐱𝐱. Imageintensity is within the visible spectrum (Ω). As shown in
Eq. (1), the dichromatic-based model describes the image123

pixel intensity by separating them into two reflection
components: 𝑤𝑤d ∫𝛺𝛺 𝑆𝑆(λ)𝐸𝐸(𝜆𝜆)𝐪𝐪(λ)𝑑𝑑λ, which denotes the
diffusedreflection
components,
and𝑤𝑤S ∫𝛺𝛺 𝐸𝐸(𝜆𝜆)𝐪𝐪(λ)𝑑𝑑λ, which denotes the specular reflection
components in an image. Diffused component includes
the reflection spectrum of the object, while the specular
one just depends on the illumination. The specular anddiffused reflection components are two independentcolor vectors, which can be expressed as two bold-facecharacters, B and G, respectively. We also assume thatthe specular components are uniformly distributed in thescene so that the illumination colors are independent to
the regional coordinates. The intensity equation of theoriginal image can be replaced a simplified combination:
𝐈𝐈(𝐱𝐱) = 𝑤𝑤dB(𝐱𝐱) + 𝑤𝑤SG,
(2)
where

𝐁𝐁�𝐱𝐱� = ∫𝛺𝛺 𝑆𝑆(λ)𝐸𝐸(𝜆𝜆)𝐪𝐪(λ)𝑑𝑑λ,13𝚲𝚲�𝐱𝐱� =𝚪𝚪 =g𝐁𝐁�𝐱𝐱�

b

𝐵𝐵r �𝐱𝐱�+𝐵𝐵g �𝐱𝐱�+𝐵𝐵b �𝐱𝐱�𝐆𝐆

(𝐺𝐺r +𝐺𝐺g +𝐺𝐺b ),,𝑚𝑚d�𝐱𝐱� = 𝑤𝑤d{𝐵𝐵r, 𝐵𝐵g, 𝐵𝐵b},𝑚𝑚s�𝐱𝐱� = 𝑤𝑤s{𝐺𝐺r, 𝐺𝐺g, 𝐺𝐺b}.

1 1 1

𝚪𝚪 ′ = { , , } and 3𝑚𝑚s = 𝑚𝑚s′. Normalized image will be
3 3 3

considered as a diffused reflection image illuminated bya standard white light.
Since Tan’s specular-to-diffuse mechanism does not
generate a real diffuse image, the image can be seemed
as a fake diffused reflection image. In this mechanism,
the maximum chromaticity is used. The definition of
maximum chromaticity of image pixels is as follow
𝜎𝜎�′(𝐱𝐱) =(3)𝑚𝑚d�𝐱𝐱�𝚲𝚲�𝐱𝐱� = �𝑚𝑚d′ �𝐱𝐱�𝚲𝚲′ �𝐱𝐱��𝚪𝚪 est ,max (𝐼𝐼 ′ r (𝐱𝐱),𝐼𝐼 ′ g (𝐱𝐱),𝐼𝐼 ′ b (𝐱𝐱)),𝐼𝐼 ′ r�𝐱𝐱�+𝐼𝐼 ′ g�𝐱𝐱�+𝐼𝐼 ′ b(𝐱𝐱)(14)where (𝐼𝐼′ r (𝐱𝐱), 𝐼𝐼′ g (𝐱𝐱), 𝐼𝐼′ b (𝐱𝐱)) is obtained in the normalized
image, σ′ denotes the chromaticity of the normalized
image. Note that σ
�′ is different from 𝜎𝜎′ and the range of�′ is not between 0 and 1. With Eqs. (11) to (13), theσmaximum chromaticity of image pixels in Eq. (14) canbe rewritten as:𝜎𝜎� ′ �𝐱𝐱� =� ′ �𝐱𝐱�+1ms′ �𝐱𝐱�md′ �𝐱𝐱�𝛬𝛬3′
md �𝐱𝐱��Λr'�𝐱𝐱�, Λg'�𝐱𝐱�, Λb'�𝐱𝐱��+𝑚𝑚s′ �𝐱𝐱�

.

(15)

�′ = max( 𝛬𝛬′r , 𝛬𝛬′g , 𝛬𝛬′b ), both md′ and ms′ can
By setting Λ
also be determined. Since the maximum chromaticity ofdiffused reflection is usually higher than that of specular1
reflection and, in generally, 𝛬𝛬̃′ > 3, and

(6)

(7)

�′
𝛬𝛬

(16)

𝜎𝜎�′diff > 𝜎𝜎�′spec ,[Λr', Λg', Λb']1>

� ′(𝐱𝐱)+ 𝑚𝑚s′
𝑚𝑚d′ 𝛬𝛬
3𝑚𝑚d′ [Λr', Λg', Λb']+𝑚𝑚s′

.

(17)

In the normalized image, (Λr'+Λg'+Λb')=1. Removing the(9)

�' -σ� 'Λ

pixel (𝐱𝐱) and substituting 𝑚𝑚s' (𝑚𝑚s'=𝑚𝑚d'( ' 1 )) into Eq. (17),
σ� -

(10)

3

we can finally obtain the equation that can represent therelationship between the image chromaticity andillumination chromaticity:

In the reflection model shown in Eq. (1), in which only
the specular reflection component exists (𝑤𝑤d=0), 𝚪𝚪 will
be independent of the specular geometrical parameter 𝑤𝑤S.
In the definition on chromaticity, the range of image
chromaticity, diffuse chromaticity, and specularchromaticity are all within the range {0, 1}. That is,{𝜎𝜎 r+𝜎𝜎 g+𝜎𝜎 b} = {𝛬𝛬r+𝛬𝛬g+𝛬𝛬b} = {𝛤𝛤 r+𝛤𝛤 g+𝛤𝛤 b} = 1. The
illuminant chromaticity is estimated based on the color
constancy method in Ref. [2] to evaluate thechromaticity 𝚪𝚪 est = �𝛤𝛤𝑟𝑟est , 𝛤𝛤𝑔𝑔est , 𝛤𝛤𝑏𝑏est �. Assume that theevaluated chromaticity is correct. The image withcorrected illumination is defined as a normalized image:1
𝐈𝐈′�𝐱𝐱� = 𝑚𝑚d′�𝐱𝐱�𝚲𝚲′ (𝑥𝑥) + 𝑚𝑚s′(𝐱𝐱),
(11)
3
where

𝚪𝚪

normalized illumination color is 𝚪𝚪est = {1,1,1} . In 𝐈𝐈′�𝐱𝐱� ,Substituting Eqs. (5) and (6) into Eq. (2), the chromaticity
equation becomes𝐈𝐈�𝐱𝐱� = 𝑚𝑚d�𝐱𝐱�𝚲𝚲�𝐱𝐱� + 𝑚𝑚s�𝐱𝐱�𝚪𝚪,
(8)
where

𝐈𝐈(𝐱𝐱)The normalized image can be denoted as 𝐈𝐈′ �𝐱𝐱� = 𝚪𝚪𝐞𝐞𝐞𝐞𝐞𝐞. The

𝐆𝐆 = ∫𝛺𝛺 𝐸𝐸(𝜆𝜆)𝐪𝐪(λ)𝑑𝑑λ.
(4)
In order to analysis the relationship between thereflection components and scaling factor. The image
chromaticity is defined by using the normalized RGB
components. Here, the chromaticity (σ), diffuse
chromaticity ( Λ ), and specular chromaticity ( Γ ) aredefined as𝐈𝐈�𝐱𝐱�
𝛔𝛔�𝐱𝐱� =,(5)
𝐼𝐼 �𝐱𝐱�+𝐼𝐼 �𝐱𝐱�+𝐼𝐼 �𝐱𝐱�r

(13)𝑚𝑚s�𝐱𝐱�𝚪𝚪 = � 𝑚𝑚s′ �𝐱𝐱�� 𝚪𝚪 est .

1𝜎𝜎�′𝐼𝐼̃′�𝐱𝐱� = 𝑚𝑚d′ �𝛬𝛬̃′ − � � ′ 1�.
3

𝜎𝜎
� −

(18)

3

In the above equation, 𝑚𝑚d′ is computed by assuming thesame chromaticity. In the specular pixel �𝐱𝐱1� anddiffused pixel �𝐱𝐱2�, the same chromaticity is used suchthat 𝛬𝛬̃′ �𝐱𝐱1� = 𝛬𝛬̃′ �𝐱𝐱2� = σ
�′(𝐱𝐱2) ( 𝑚𝑚s'=𝑚𝑚d' here). We candetermine 𝑚𝑚d' by using:𝑚𝑚d'�𝐱𝐱1� =The final result is𝐼𝐼̃′�𝐱𝐱1�[3𝜎𝜎� ′ �𝐱𝐱1�−1]� ′ �𝐱𝐱1�−1]𝜎𝜎� ′ �𝐱𝐱1�[3𝛬𝛬

.

(19)

𝑚𝑚S′ �𝐱𝐱1� = [𝐼𝐼′ r�𝐱𝐱1� + 𝐼𝐼′ g�𝐱𝐱1� + 𝐼𝐼′ b�𝐱𝐱1�]𝑚𝑚d′ �𝐱𝐱1�, (20)(12)

where the diffused reflection component is124

′𝑚𝑚S �𝐱𝐱1�
𝑚𝑚d′ �𝐱𝐱1�𝛬𝛬̃′ �𝐱𝐱1� = 𝐼𝐼̃′�𝐱𝐱1� −.
3chromaticity with the new one 𝚪𝚪 set = �𝛤𝛤𝑟𝑟set , 𝛤𝛤𝑔𝑔set , 𝛤𝛤𝑏𝑏set �, thefinal SFI image can be obtained.

(21)

(a )
(b)
(c)
(d)
Fig. 2: (a) The original image; (b) Projection of pixel intensityin (a) into the chromatic space (σ ̃', I ̃'); (c) The SFI with the
maximum chromaticity; (d) The projection of pixel intensity in(c) into the chromatic space (σ ̃', I ̃').

(a)
(b)
(c)
Figure 3: (a) Intensity of SFI; (b) SFI; (c) invariant specularpart 𝐈𝐈𝑚𝑚𝑚𝑚𝑚𝑚 .

Figures 2(a) and 2(b) show an input image and its 2D
projection of the maximum chromatic intensity �σ′ andintensity 𝐼𝐼̃′ , respectively. In Fig. 2(b), the x axis denotes
the maximum chromaticity intensity σ
�′, while the y axis
denotes the intensity 𝐼𝐼̃′ . Fig. 2(c) shows the SFI obtained
�′ =by suing Tan’s method with setting the Λmax(𝛬𝛬′r , 𝛬𝛬′g , 𝛬𝛬′b ) in the maximum chromaticity for all
image pixels. In Fig. 2(d), the SFI specular component
has the same chromaticity with the diffused one.
Yoon proposed a simplified SFI method based on theproperty of invariant specular reflection, whosemechanism is shown as follows:
𝐈𝐈𝑠𝑠𝑠𝑠 �𝐱𝐱� = 𝐈𝐈′�𝐱𝐱� − 𝐈𝐈𝑚𝑚𝑚𝑚𝑚𝑚 �𝐱𝐱�,1

𝐈𝐈𝑚𝑚𝑚𝑚𝑚𝑚 �𝐱𝐱� = 𝑚𝑚d 𝜆𝜆𝑚𝑚𝑚𝑚𝑚𝑚 �𝐱𝐱� + 𝑚𝑚s′ �𝐱𝐱�,
3

(a)

r

g

b

(23)

3. Experiment Results(24)

Suppose that the reflection light is generated from a fixedillumination in a scene. The proposed system can
automatically detect the light source and then perform
image normalization so that the image can be separated
into the normalized image, diffused image, and specular
image. By setting the chromaticity and the reflectivity,
the user can obtain the image with a modified
illumination color and with a stronger or weaker speculareffect.
Figures 4 and 5 provide two demonstrations of the
proposed method on resetting the chromaticity. Figure4(a) shows an input image, in which the single-color
spherical object is illuminated by a halogen lamp with thecolor temperature 4700 °K. Figure 4(b) shows thenormalized image using the designated illumination
chromaticity RGB = (0.5324, 0.3077, 0.1594). Figures
4(c) and 4(d) show the extracted diffused and specularcomponents, respectively. Figure 4(e) shows the virtual
image obtained by using the modified illumination

Different from the Tan’s method, we use the HSV color
space to determine the SFI, which is shown in Fig. 3(b).
The S and V components present the saturation and scalarintensity values, respectively. And the specular anddiffused components of chromaticity are the same withthat in the input image. By subtracting the change of
specular component, the intensity of SFI shown in Fig.
3(a) will be a uniform intensity. Fig. 3(c) shows theinvariant specular reflection part of the original image.
The SFI component is determined as follows:
𝐈𝐈𝑠𝑠𝑠𝑠 �𝐱𝐱� = 𝑚𝑚d �𝜆𝜆�𝐱𝐱� − 𝜆𝜆𝑚𝑚𝑚𝑚𝑚𝑚 �𝐱𝐱��.

(c)

(d)
(e)
(f)
Fig. 4: (a) Image of a spherical object illuminated by a halogen
lamp; (b) Normalized image with chromaticity RGB = (0.5324,
0.3077, 0.1594); (c) Separated diffused component; (d)Separated specular component; (e) The virtual image with themodified illumination chromaticity RGB = (0.2187, 0.2695,
0.7812); (f) Image with 50% reflectivity of the normalized
image in (b).

(22)

where 𝐈𝐈𝑠𝑠𝑠𝑠 presents SFI, 𝐈𝐈𝑚𝑚𝑚𝑚𝑚𝑚 presents the minimum pixel
value of the normalized image in RGB channels.
Similarly, we assume that the specular reflection is also
invariant in the HSV space:
𝑆𝑆 = 0
𝐈𝐈𝑚𝑚𝑚𝑚𝑚𝑚 �𝐱𝐱� = � 𝑉𝑉 = min(𝐼𝐼 , 𝐼𝐼 , 𝐼𝐼 ) .

(b)

(25)

The illumination setting for the final output SFI imageis the last step. The user can define a new reflectivity by
setting ∑ { } 𝚪𝚪set . That is, the separating specular anddiffused reflections parts can be edited for the final
combination. Finally, by replacing the originalu∈ r,g,b125

chromaticity RGB = (0.2187, 0.2695, 0.7812). Figure 4(f)shows the image with 50% reflectivity of the normalized
image shown in Figure 4(b). There are two colors in thespherical object shown in Figure 5(a). The illumination is
the same as that in Figure 4(a). However, the designatedillumination chromaticity RGB= (0.2187, 0.2695, 0.7812)
in Figure 5(b) is different from that in Figure 4(b). By
using the proposed method, Figures 5(c) and 5(d) show
the separated diffused and specular components,
respectively. Similarly, Figures 5(e) and 5(f) show theimages obtained by using the modified illumination
chromaticity RGB = (0.3046, 0.3007, 0.6992) and with50% reflectivity of the normalized image in Figure 5(b),respectively.

(a)

(b)

the modified illumination chromaticity RGB = (0.7304,
0.2695, 0.4062) of the normalized image in Fig. 7(b).

(a)

(b)

(c)

(d)
(e)
(f)
Fig. 6: (a) The image of a mask object illuminated by aincandescence bulb; (b) Normalized image with chromaticity
RGB = (0.3764, 0.3432, 0.294); (c) Separated diffusedcomponent; (d) Separated specular component; (e) The virtual
image with the modified illumination chromaticity RGB =(0.2109, 0.7773, 0.2265); (f) Image with 200% reflectivity ofthe original image in (a) .

(c)

(d)
(e)
(f)
Fig. 5: (a) Dichromatic image of a spherical object illuminatedby a halogen lamp; (b) Normalized image with chromaticity
RGB = (0.2187, 0.2695, 0.7812); (c) Separated diffusedcomponent; (d) Separated specular component; (e) The virtual
image with the modified illumination chromaticity RGB =(0.3046, 0.3007,0.6992); (f) Image with 50% reflectivity of thenormalized image in (b).

(a)

Figures 6 and 7 shows the two examples with a more
complicated geometric shape and with multi-color
texture in the object, respectively. Figure 6(a) shows asingle-color mask image illuminated by an
incandescence bulb. Figure 6(b) shows the normalized
image using the designated illumination chromaticity
RGB = (0.3764, 0.3432, 0.294). Figures 6(c) and 6(d)show the extracted diffused and specular components,
respectively. Figure 6(e) shows the virtual image
obtained by using the modified illumination chromaticity
RGB = (0.2109, 0.7773, 0.2265). Figure 6(f) shows theimage with 200% reflectivity of the normalized image
shown in Figure 6(a). In Figure 7(a), there are multiple
colors in the object. The illumination is the same as thatin Figure 6(a). Figure 7(b) shows the normalized image
with the same chromaticity in Figure 7(a). By using the
proposed method, Figure 7(c) and 7(d) show the
separated diffused and specular components, respectively.
Finally, Figure 5(e) show the images obtained by using

(c)

(b)

(d)

(e)
Fig. 7: (a) The image of a multicolor object illuminated by aincandescence bulb; (b) Normalized image with the same
chromaticity in (a); (c) Separated diffused component; (d)Separated specular component; (e) The virtual image with themodified illumination chromaticity RGB = (0.7304, 0.2695,

1260.4062) of the original image in (a).

R.T. Tan, K. Ikeuchi, and K. Nishino, “Color constancy
through inverse-intensity chromaticity space,” Journal of
the Optical Society of America, A, vol. 21, no. 3, pp. 321334, 2004.
9. Y. Akashi and T. Okatani, “Separation of reflection
components by sparse non-negative matrix factorization,”
In: Cremers D., Reid I., Saito H., Yang MH. (eds)
Computer Vision -- ACCV 2014. ACCV 2014. Lecture
Notes in Computer Science, vol. 9007, pp 611-625, 2015.
10. H. Wang, C. Xu, X. Wang, Y. Zhang, B. Peng, “Light fieldimaging based accurate image specular highlight removal,”
PLOS ONE, vol. 11, no. 6, e0156173, 2016.
11. J. Suo, D. An, X. Ji, H. Wang, and Q. Dai, “Fast and highquality highlight removal from a single image,” IEEE
Transactions on Image Processing, vol. 25, no. 11, pp.
5441-5454, 2016.
12. W. Ren, J. Tian and Y. Tang, “Specular reflection
separation with color-lines constraint,” IEEE Transactions
on Image Processing, vol. 26, no. 5, pp. 2327-2337, 2017
13. C. Li, S. Lin, K. Zhou and K. Ikeuchi, “Specular Highlight
Removal in Facial Images,” 2017 IEEE Conference on
Computer Vision and Pattern Recognition (CVPR),
Honolulu, HI, 2017, pp. 2780-2789.
14. J. Guo, Z. Zhou, and L. Wang, “Single image highlightremoval with a sparse and low-rank reflection model,”ECCV 2018, vol. 4, pp. 282-298, 2018

4. Conclusion
We propose a method to simulate the object images undervarious illumination colors without constructing the 3-Dmodel of the scene or requiring multiple input images. In
addition, the reflectivity can also be assigned and be
combined with the separated diffused components toobtain the designated image. Several examples withdifferent objects are implemented in our experiments sothat the effectiveness of the proposed method have beenverified. The improved mechanism of SFI can accelerate
the iteration process. Given an input image, the system
will separate the specular and reflection components at
first. Thus the high-light removal can be achieved. A
graphical user interface has been implemented so that theusers can easily manipulate the system to obtain theimages with various illumination conditions such as thechromaticity and reflectivity.
Acknowledgements
This research was financially supported by Ministry of
Science and Technology, Taiwan under the contract
number MOST 109-2221-E-224-046-MY2.

Authors Introduction

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Dr. Hsuan T. Chang

He is a Professor in Department
ofElectricalEngineering,
National Yunlin University of
Science and Technology in
Taiwan. He received his Ph.D
degree from the Department ofElectrical Engineering, National
Chung Chen University, Taiwan
in 1997. His research interests
include digital image processing and optical information
processing.
Mr. Wei-Hsiu Chang

127

He received his Master’s degreefrom the Department of ElectricalEngineering, National Yunlin
University of Science andTechnology, Taiwan in 2003. Hecurrently is a CAD engineering in
Innolux Corporation, Jhunan,
Taiwan.