Augmented Reality Based on Estimation ofDefocusing and Motion Blurring from Captured Images

Bunyo OKUMURA ∗ Masayuki KANBARA ∗ Naokazu YOKOYA ∗

Graduate School of Information ScienceNara Institute of Science and Technology

8916–5 Takayama, Ikoma, Nara, 630–0192, Japan

ABSTRACT

Photometric registration is as important as geometric registration togenerate a seamless augmented reality scene. Especially the dif-ference in image quality between a real image and virtual objectscaused by defocusing and motion blurring in capturing a real sceneimage easily exhibits the seam between real and virtual worlds.To avoid this problem in video see-through augmented reality, itis necessary to simulate the optical system of camera when virtualobjects are rendered. This paper proposes an image compositionmethod for video see-through augmented reality, which is based ondefocusing and motion blurring estimation from the captured realimage and rendering of virtual objects with blur effects. In exper-iments, the effectiveness of the proposed method is confirmed bycomparing a real image with virtual objects rendered by the pro-posed method.

CR Categories: H.5.1 [Information interfaces and presentation]:Multimedia Information Systems—Artificial, augmented, and vir-tual realities

Keywords: augmented reality, photometric registration, blur esti-mation, point spread function, image quality

1 INTRODUCTION

Augmented reality is a technology which provides users withlocation-based information by overlaying virtual objects on the realworld[1, 2]. In augmented reality, it is important to resolve someregistration problems including geometric and photometric regis-tration problems. The geometric registration means that the posi-tion and the orientation of virtual objects are consistent with thereal world under varying viewpoints. The photometric registrationmeans that shadow and shading of virtual objects and their imagequality are consistent with the real world.

Recently, there are a number of researches that are intended toincrease the photometric consistency[3, 4, 5]. However, they haveconcentrated mainly on shadow and shading of virtual objects andthere are few attempts focused on the quality of virtual objects inthe composed image. Especially, in video see-through augmentedreality, the seamlessness of the real and virtual worlds is affectedby the differences in image quality between superimposed virtualobjects and the real image. This problem is caused by the dif-ference between real and virtual camera models. In general, anideal camera model which means that the image quality is not de-graded by the lens is used when rendering virtual objects. How-ever, there is deterioration in an image captured by a real camera.Some attempts have been done to reduce the difference betweenreal and virtual worlds, which change the image quality of realimage and rendered virtual objects with “cartoon-like” or “sketch-like” representation[6, 7].

∗e-mail:{bunyo-o, kanbara, yokoya}@is.naist.jp

In order to resolve the real and virtual camera model inconsis-tency, it is necessary to estimate the deterioration of captured imageand to represent the blur effect on virtual objects. In image pro-cessing and computer vision, image restoration methods based onestimation of image degradation described by the point spread func-tion (PSF) have been proposed[8]. However, these techniques needmuch calculation cost because most of them are based on iterativecomputation. Thus such techniques are not suitable for augmentedreality which requires real time operation. On the other hand, incomputer graphics, there are some researches which achieve real-istic rendering of virtual objects simulating the real camera[9, 10].However, it is also difficult for these techniques to apply to aug-mented reality because lens parameters, which are changed by lensoperations, always need to be known and intrinsic camera parame-ters should be calibrated in advance in the related work.

To realize consistent image quality in augmented reality, this pa-per copes with both defocusing and motion blurring caused by thecamera, that are typical factors of the deterioration of image. Wepropose a technique which equalizes the quality of the real imagecaptured by the real camera and the rendered virtual objects. Theproposed method merges a captured image and rendered virtual ob-jects with blur effects estimated from a real scene image contain-ing a marker. We also propose an improved geometric registrationmethod which can detect the image marker accurately by eliminat-ing the blur effect from the captured image by using the estimatedblur parameters.

This paper is structured as follows. Section 2 briefly reviewsrelated work. Section 3 describes a blur model of image, a blurestimation method, and a method for estimating the camera posi-tion and orientation from an image marker. A method of renderingvirtual objects based on reproduction of estimated blur is explainedas well. In Section 4 experimental results are described. Finally,Section 5 gives conclusion and future work.

2 RELATED WORK

Estimation of image degradation has been studied in computer vi-sion. In computer graphics, some techniques which can render vir-tual objects with blur effects have been proposed.Estimations of blur from images: In computer vision, in order torestore the degraded image, most methods estimate the PSF whichmodels the degradation of image and recover the image by remov-ing the effects of the PSF. In general, these methods can be di-vided into two approaches according to assumptions. One esti-mates the blur effect based on analyzing patterns on a target objectas knowledge. The other does not have any knowledge about thetarget objects. One of the former approaches is an estimation ofthe PSF of a scanner[11]. This technique can estimate the scan-ner property described by the PSF scanning known patterns. Thelatter approach is called blind deconvolution, which attempts to re-cover target objects from a set of blurred images. There are a num-ber of techniques which treat the same problem. The depth fromdefocusing[12, 13, 14] and the super-resolution[15, 16, 17] are typ-

ical techniques, which also estimate the PSF simultaneously.Blur representation on virtual objects: In computer graphics,there are a number of techniques to reproduce various effectscaused by a camera. These techniques contribute to render virtualobjects realistically. For example, Nakamae et al.[18] have pro-posed a photo montage technique for landscape environment as-sessment. This technique can improve the quality of final mon-tage image with consideration of the seamlessness of the borderbetween a photograph and superimposed virtual objects. Further-more, there are a number of researches to generate more realisticeffects caused by a real camera assuming lens model of the cam-era. For example, Kolb et al.[9] have proposed a rendering tech-nique simulating an optical system of real camera. This methodcan reproduce the depth of focus effect which is characterized bythe design of lens. Asada and Baba[10] have proposed a renderingtechnique with a new camera model which has zoom, focus and irisparameters. Their method can treat the relation among lens param-eters systematically and achieve rendering of virtual objects withthe depth of field effect in real time.

The proposed method in this paper combines blur estimationin computer vision with blur representation in computer graphics.Note that most of PSF estimation methods in computer vision areaimed at arbitrary PSFs. These methods take much calculation costand thus it is difficult to apply such methods for augmented real-ity which requires real time processing. In the proposed method,in order to achieve the real time processing, the PSF which rep-resents both defocusing and motion blurring is modeled approxi-mately and the parameter of PSF is estimated by using an imagemarker whose color and shape are known. On the other hand, incomputer graphics, most techniques for rendering blur effects as-sume off-line processing. In addition, many parameters changed bylens operations of the camera are required to simulate blur effects.In the proposed method, the blur effect is represented by applyinga simple filter determined by the PSF to rendered virtual objects.Moreover, the calculation cost of this processing is reduced by us-ing graphics hardware acceleration.

3 AUGMENTED REALITY BASED ON BLUR ESTIMATION

The proposed method reduces the difference in image quality be-tween the real and virtual objects in augmented reality by estimat-ing the PSF from an observed image marker in a real scene, andreproducing the PSF to virtual objects. We use pixel intensitiesaround the edge of image marker to estimate the PSF. When theimage is blurred, the intensity changes gradually along the direc-tion orthogonal to the edge. In this paper, the change in intensityprofile on the edge is described as the width of blur. The shape ofPSF is estimated by integrating the width of blur in edges in variousdirections.

In some augmented reality systems, virtual objects are composedaround image markers in the real world[19]. In this case, we canassume that the blur effect on virtual objects is represented by thedegree of blur at the marker because the PSF is related to the depthof the target object and the change of depth is not large around themarker in most case.

Fig. 1 illustrates a flow diagram of the proposed method. First,the real scene is captured by a camera, and image markers whosecolor and shape are known are detected (Fig. 1 (A)). In the secondstep, the width of blur is estimated by fitting a function defined by amodel of PSF to intensities in an edge region of the marker, and theparameters of PSF are acquired by integrating the estimated widthof blur in various directions (Fig. 1 (B)). Next, the camera posi-tion and orientation in marker coordinate system are estimated byrecovering the shape of the marker using the estimated blur param-eters (Fig. 1 (C)). Finally, virtual objects are rendered with the blureffect and are composed with the real image (Fig. 1 (D)). The blur

(A) Detection of image marker

(B) Estimation of PSF parameters from captured image

(C) Estimation of camera position and orientationin consideration of blur effect

(D) Rendering of virtual objects with blur effect

Figure 1: Flow diagram of the proposed method.

Lens Focal planeObject

Optical Axis

w vf

Image plane

Optical diskcaused by the defocusing(radius of optical disk: r)

wf v: focal length of the lens : distance from the lens to the image plane

: depth from the lens to the object

Figure 2: Defocusing caused by the lens.

model of a camera and the image marker as well as processes (A)to (D) are explained in the following sections.

3.1 Model of defocusing and motion blurring

In general, the blur effect caused by the camera includes two ma-jor factors: defocusing and motion blurring. The blurred image isgenerated by convolution of an ideal image with the PSF which ex-presses effects of defocusing and motion blurring. The blur causedby defocusing depends on depth from the center of lens to an ob-ject, the aperture of lens, and the resolution of camera. The blur ofimage is caused by defocusing of lens as shown in Fig. 2. Note thatthe radiusr of an optical disk on the image plane is defined by Eq.(1), andr depends on the position in the image[20].

r = ργv

∣∣∣∣1f− 1

v− 1

w

∣∣∣∣ , (1)

where f ,w andv mean the focal length of lens, the depth from lensto an object and the distance from lens to the image plane, respec-tively. γ and ρ mean the aperture of lens and a constant whichdepends on the resolution of CCD, respectively.

In general, two dimensional circular spread of light is used asa kernel of PSF (pill-box) of defocusing. In addition, the radiusrof optical disk depends on the position in the image. In this paper,the PSF is assumed to be a function which does not depend on theposition in the image[21], since virtual objects are placed around amarker in the captured image; that is, we assume that the radius ofoptical diskr is constant around the marker. The PSF of blur causedby defocusing of camera is defined as follows:

R(x,y; r) ={

1πr2 ; wherex2 +y2 ≤ r2

0 ; otherwise, (2)

wherex andy represent the position of target pixel, andr showsthe radius of the PSF which is equivalent to the radius of opticaldisk. In this paper,r is considered as the parameter of blur causedby defocusing.

The motion blurring depends on the motion of camera, move-ment of target object and the exposure of camera. The motion ofcamera and the movement of target object are generally very com-plicated, and so the formulation of the PSF is very complex. Inthis paper, we assume that the motion of camera and the movementof target object can be represented by one-dimensional translationin the image plane of camera. The PSF of motion blurring is nowagain as follows:

L(x,y; l ,θ) =12l

(u(x′+ l)−u(y′− l)

)δ (y′), (3)

where(

x′y′

)=

(cosθ sinθ−sinθ cosθ

)(xy

). (4)

Note thatx andy represent the position of target pixel. The func-tion u(x) shows the unit step function andδ (t) denotes the Dirac’sdelta function.l andθ mean the length of uniform motion and thedirection of motion, respectively. Note thatl andθ are consideredas parameters of blur effect by motion blurring.

This paper treats the blur effect caused by both defocus and cam-era motion. The model of PSF is obtained by convoluting Eq. (2)with Eq. (3). However, the calculation cost of convolution requiredin estimation of the PSF parameters is not small, thus it is not ac-ceptable for augmented reality. In order to achieve the real timeprocessing, we simplify the PSF. In the proposed method, the fol-lowing approximated PSF is employed.

P(x,y; r, l ,θ)

=

{1

π((r+l)2+r2) ;(

x′r+l

)2+

(y′r

)2 ≤ 1

0 ; otherwise, (5)

where(

x′y′

)=

(cosθ sinθ−sinθ cosθ

)(xy

). (6)

Here,x andy represent the position of target pixel in the image.r,l andθ show the radius of defocusing blur, the length of uniformmotion and the direction of motion, respectively. In this paper,r, landθ are considered as parameters of the PSF of blur. The aboveequation means an elliptical spread of light on an image plane. Thedirection of major axis of the ellipse shows the motion direction ofcamera. From this approximation of PSF, the profile of blurred edgecan be described by a formula, so that calculation cost of estimatingthe PSF parameters can be reduced.

3.2 Image marker

A square marker with known shape and color is used to estimate thecamera position and orientation[19]. Fig. 3 illustrates the pattern ofthe marker used in this study. The edge of inner circle in the markeris used to estimate the parameters of PSF. The role of each part ofthe marker is as follows.

(a) ID area: This area is used to identify the marker by recogniz-ing the pattern of black circle.

(b) Circular edge: This edge is used to estimate parameters ofPSF. The proposed method acquires the PSF from the param-eters estimated at multiple points on the edge in various direc-tions.

(c) Corners of square marker: These corners are used to deter-mine the camera position and orientation in the marker coor-dinate system for geometric registration.

(a) ID area

(b) Circular edge for estimating the parameters of PSF

(c) Corners for estimatingthe position and orientationof camera

Figure 3: An example of marker.

3.3 Detection of marker from captured image: Process (A)

Marker of known shape and color is detected by the following stan-dard processes.

1. Extraction of marker region: An image captured by cam-era is binarized and is labeled. Regions whose size is largeenough are detected as candidates of marker.

2. Detection of marker corners: Each edge of the marker isdetected by fitting a straight line. Corners of marker are thendetermined by calculating intersections of detected lines.

Note that the position of corner of marker may include some errorscaused by the blur effect. When a threshold is not suitable in the bi-narization process, the marker will be shrunk or expanded becausethe intensity of blurred edge changes gradually. Since the cameraposition and orientation are estimated by solving the PnP problem(n = 4)[19], the accuracy of position and orientation of camera isaffected by the accuracy of detected corner positions in the image.In this paper, this problem is resolved by eliminating the blur effectin process (C) described in Section 3.6.

3.4 Estimation of width of blur: Process (B)

The width of blur is estimated in various directions at multiplepoints on the circular edge of marker before estimating parametersof PSF. This section explains estimation of the width of blur fromchange of intensity along the direction orthogonal to the edge.

Provided that an ideal edge profile of marker is expressed by thestep function shown in Fig. 4(a), the intensity of edge affected bythe blur effect expressed by the PSF[13] is given as follows:

f (~p;d,~p0, imin, imax)

=

imin ; t <−1g(t)(imax− imin)+ imin ; −1≤ t ≤ 1imax ; t > 1

, (7)

where

t =(~p−~p0) ·~o

d, (8)

g(t) =12

+1π

(t√

1− t2 +arcsint)

, (9)

and~o is the normalized direction orthogonal to the edge.~p and~p0 are the position of pixel and the position of edge which is notaffected by the blur effect, respectively. The parameterd meansthe width of blur which is equivalent to the width of slope of theintensity along the direction orthogonal to the edge.imin and imaxshow the minimum and maximum values of intensities of the edge.Parameters of blur are estimated by fitting a function shown in Eq.(7) to the edge profile in the image. Fig. 4(a) illustrates the pro-files of ideal and blurred edges. Parametersd,~p0, imin andimax are

Position of pixel ( )

Inte

nsit

y of

pixe

l Ideal edge

Intensity of pixel

i m in

i m ax

( )pfr

ipr0p

rd

Edge position

Width of blur

(a) Edge profile and its function fitting.

(b) Sampled pixelsused in blurestimation.

Figure 4: Function fitting to blurred edge and sampled pixels usedin blur estimation.

estimated by minimizing the error defined by Eq. (10) using thequasi-Newton method.

Eedge(d,~p0, imin, imax)

=N

∑i=1{I(~pi)− f (~pi ;d,~p0, imin, imax)}2 . (10)

In Eq. (10),~pi , I(~pi) andN mean the position of thei-th pixel, theintensity of the pixel along the direction orthogonal to the edge andthe number of sampled pixels, respectively. Lines in Fig. 4(b) in-dicate pixels used for blur estimation. The position of circular edgein the image marker is determined from a marker region detected inprocess (A) described in Section 3.3.

3.5 Estimation of PSF parameters: Process (B)

The blur sizes estimated at multiple points on the edge of circle areintegrated to determine parameters of the PSF. Note that the kernelof PSF has an elliptical shape and the eccentricity of the ellipse isthe same as the estimated width of blur. The parametersr, l andθof the PSF are estimated by minimizing the error function definedby Eq. (11) using the quasi-Newton method.

EPSF(r, l ,θ) =M

∑j=1

{d j −h(θ j ; r, l ,θ)

}2

Eedge, j, (11)

where

h(θ j ; r, l ,θ) =1√(

cos(θ j−θ)r+l

)2+

(sin(θ j−θ)

r

)2, (12)

and j represents the index of edge.r, l andθ mean parameters ofthe PSF described in Section 3.1.h(θ j ; r, l ,θ) represents the eccen-tricity of the ellipse about the directionθ . d j andEedge, j denote thewidth of blur estimated fromj-th point on edge and the error in theestimation mentioned in Section 3.4, respectively.

3.6 Estimation of camera position and orientation in consid-eration of blur effects: Process (C)

In order to estimate the camera position and orientation accurately,it is necessary to recover the shape of marker by removing the in-fluence of blur. There are two approaches to recover the markershape. One is an image processing method based on inverse fil-tering. The marker is restored by applying the inverse filter to theimage degraded by blur effects. The other is a restoration methodusing geometrical features used to estimate the edge of marker. Inthis paper, to achieve a real time processing required by the aug-mented reality, the latter based on the estimation of edge position isapplied. Corners of marker are detected from intersections of lines

which represent edges of the marker. The camera position and ori-entation are estimated from positions of corners of the marker inthe image. The detail of processing is as follows.

1. The process described in Section 3.4 is applied to outer edgesof the marker since this process can estimate the accurate edgeposition~p0 under blur effect.

2. The lines of outer edge of the marker are estimated by fittinglines to edge positions.

3. Four corners of the square marker are determined from inter-sections of lines, and the camera position and orientation inthe marker coordinate system are then computed by solvingthe PnP problem (n = 4).

Note that the camera position and orientation can be more accuratethan those estimated by standard techniques such as [19] becausethe positions of corners estimated by this process are not influencedby the blur effects.

3.7 Rendering of virtual objects with blur effects: Process (D)

In order to achieve real time computation, the processes of ren-dering and simulation of blur effects are computed using graphicshardware. First, virtual objects are rendered to a texture buffer withthe camera position and orientation estimated in Section 3.6. Then,a smoothing filter with the estimated PSF is applied to the render-ing result. The kernel of PSF is the same as Eq. (5). Here, only aneighborhood of4(r + l)2 pixels is calculated to accelerate the com-putation. Furthermore, the branching function of OpenGL shadinglanguage is used so that pixels outside of the PSF are excluded.

4 EXPERIMENTS

In experiments, in order to confirm that the proposed method candecrease the difference in image quality between the real imageand virtual objects, we have compared the real image with a vir-tual object rendered by the proposed method. Experiments are car-ried out using a desktop PC (CPU : PentiumD 3.0GHz, Memory :3.25GByte, Graphics card : Radeon X1900XTX) and a USB cam-era (ARGO Lu-135c, Resolution : 1024× 768 pixels, Capturingframe rate: 15fps). Square markers are placed in the real world anda virtual object is composed by recognizing the markers. More-over, the graphics hardware is used when the filtering process ofblur effects is applied to virtual objects as mentioned in the previ-ous section. First, we confirm the accuracy of parameters of PSFestimated by the proposed method described in Sections 3.4 and3.5.

4.1 Evaluation of camera position estimation using blur pa-rameters

In this section, in order to verify the accuracy of camera positionestimation using blur parameters, we have carried out a quantitativeevaluation. The estimated distance from camera to marker is inves-tigated under varying the focus of camera and fixing the positionsof camera and marker. Here, the distance from camera to marker isset to 900mm.

Fig. 5 shows the relationship between the estimated width ofblur and the estimated camera position when the focus of camerachanges. Figs. 5 (a) and (b) are cases when the focus is changed tonear and far, respectively. They also illustrate selected 250 samplesunder various focus values. From these results, we can confirm thatthe distance from camera to marker by the conventional method[19]increases when the size of defocusing is large. This is become theregion of marker is detected by a simple binarization method with

890

900910

920

930

940950

960

2 3 4 5 6 7 8 9 10 11 12 13 14

Estimated size of defocusing (r ) [pixel]

Est

imat

ed d

ista

nce

from

cam

era

to m

arke

r [m

m] Proposed Method

Conventional method

(a) Shortsighted focusing.

890

900910

920

930

940950

960

2 3 4 5 6 7 8 9 10 11 12 13 14

Estimated size of defocusing (r ) [pixel]

Est

imat

ed d

ista

nce

from

cam

era

to m

arke

r [m

m] Proposed Method

Conventional method

(b) Longsighted focusing.

Figure 5: Estimated distance from camera to marker under changeof focus of camera.

a threshold and thus extracted region is smaller than the actual sizeof the marker. On the other hand, it is clearly shown in Fig. 5 thatthe distance is estimated accurately by the proposed method even ifthe width of blur is large.

4.2 Evaluation of estimated parameters of PSF in simulation

To verify the accuracy of parameters of PSF, we have carried out aquantitative evaluation of estimated parameters in simulation. Im-ages used in this experiment are generated with various blur values.The parameter of defocusingr is changed by 0.5 from 0.5 to 4.5.Parameters of motion blurringl andθ are changed by 0.5 from 0to 4.5 and byπ/8 from 0 to π, respectively. In this evaluation, wehave computed the difference between ground truth and the esti-mated parameters. The difference larger than zero means that theestimated parameter of blur is larger than the simulated parameter.

Fig. 6 illustrates the relationship between the simulation andestimated parameters. In Fig. 6(a), each sequence shows the varia-tion of the parameterr for different values of parameterl . Exceptfor the case where parameterr is 0.5, the difference of parameterrbetween the simulation and estimated parameter is about0.5. Fig.6(b) shows the difference of the parameterl . Each sequence showsthe variation of the parameterl for different values of parameterr. The difference of parameterl between the simulation and esti-mated parameter is about−0.5 excepting the case where parameterr is 0.5. In addition, the difference of the parameterθ between thesimulation and estimated parameter is from−0.24 to 0.24 radians.Here, estimated parameters of PSF include errors because the esti-mation of width of blur becomes unstable when the parameterr issmaller than 1. However, we consider that the estimated parametersof PSF are almost accurate in order to reproduce both defocusingand motion blurring.

4.3 Comparison between real and virtual objects in aug-mented scene image

To confirm the effectiveness of the proposed method, rendered vir-tual objects are compared with a real image fixing and changing the

-0.5

0

0.5

1

1.5

2

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5The size of defocusing (r ) [pixel]

Mea

n er

ror

in e

stim

ated

para

met

er o

f de

focu

sing

[pix

el]

l = 0l = 0.5l = 1l = 1.5l = 2l = 2.5l = 3l = 3.5l = 4l = 4.5

(a) The difference in the size of defocusing (r).

-1

-0.5

0

0.5

1

1.5

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5The size of motion blurring (l ) [pixel]

Mea

n er

ror

in e

stim

ated

Par

amet

er o

f m

otio

nbl

urri

ng [

pixe

l]

r = 0.5r = 1r = 1.5

r = 2r = 2.5r = 3r = 3.5

r = 4r = 4.5

(b) The difference in the size of motion blurring (l ).

Figure 6: Errors between estimated parameter and parameter usedin simulation.

focus of camera. We compare real objects captured by the camerawith composed virtual objects. Composed results of virtual objectsby a conventional method which does not represent the blur of realimage are also shown.Static scene with motion blurring: Fig. 7 shows real and aug-mented scene images under motion blurring. Figs. 7(a), (b) and (c)show a captured image, an augmented image without considerationof motion blurring and an augmented image with consideration ofmotion blurring, respectively. We can see the difference betweenthe real object and the virtual object in Fig. 7(b). For example, thepattern in the real cube circumscribed by (A) in Fig. 7(a) is affectedby the blur effect and horizontal edges in the image are disappeared.However, edges of the virtual object circumscribed by (B) in Fig.7(b), which is rendered without motion blurring estimation, existdistinctly. On the other hand, edges of the virtual object circum-scribed by (C) in Fig. 7(c), which is rendered with motion blurringestimation, are disappeared and the appearance is similar to that ofthe real object.Static scene with different focus of camera:Fig. 8 shows theexperimental scene. A cylindrical real object is located at 150mmfrom the camera. The marker and the real object for evaluation areat 300mm from the camera. A checker board is placed at 900mmfrom the camera. The real object to be compared is a cube whosesurfaces have grid patterns, and the same object is merged into areal image as a virtual object. When the virtual object is rendered,the light environment is manually tuned and the shadow was notrendered.

Fig. 9 shows augmented scene images with different focuses.Fig. 9(i) is focused on a near object, (ii) is focused on a marker, and(iii) is focused on a far object. In augmented scenes without blurestimation in column (a) of Fig.9, though a blur effect on the realobject (right cube) is caused by defocusing, the virtual object (leftcube) is composed without such an effect. Therefore, there can beobserved the inconsistency of image quality between the real imageand the virtual object and the seamlessness is not achieved. On theother hand, in column (b) of Fig. 9, the incompatibility caused bythe difference in image quality has been reduced because the im-age quality of virtual object is matched with the real image. In theproposed method, the reproduced blur effect is uniform around themarker, although the difference in image quality is reduced. The

(A)(a) Captured image

(Camera motion is upward in the image.)

(B)(b) Rendered image without motion blur

estimation.

(C)(c) Rendered image with motion blur

estimation.

Figure 7: Results of merging a virtual object into a real scene image with motion blurring

300mm150mm 900mmFigure 8: Experimental environment

frame rate is approximately 15fps for rendering without blur esti-mation and 10fps for rendering with blur estimation. The frame rateis downed because the proposed method requires additional compu-tational cost for estimating parameters of PSF and for applying blureffects on virtual objects.

5 CONCLUSION AND FUTURE WORK

This paper has proposed an image composition method which isbased on estimation of blur from a real image for video see-throughaugmented reality. The proposed method can estimate parametersof approximated PSF by fitting a function to edges in a marker andby fitting an ellipse to the width of blur in various directions of themarker edge. We have also proposed a new method for estimatingthe camera position and orientation, which can eliminate the blureffect from the captured image. In experiments, have confirmed thatthe proposed method correctly estimates the parameters of PSF insimulation. Consequently, the method can seamlessly merge virtualobjects into the real image which is affected by blur effects causedby defocusing and by motion blurring of camera. This has beenproven through experiments.

In the present study, the approximated PSF has an elliptical shapeand does not depend on the depth in the image. There are some limi-tations caused by this assumption. The virtual objects can be placedonly around the marker, and PSFs of real cameras may not have anelliptical form. The following topics should further be investigatedin future work.

• Consideration of arbitrary PSFs

• Calibration of intrinsic parameters of camera (f , v, ρ andγshown in Section 3.1)

The former makes it possible to represent blur effects more accu-rately, and the latter makes it possible to apply blur effects on virtualobjects not only around the marker but also at arbitrary positions.

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(i) Focused on a near object.

(ii) Focused on a marker.

(iii) Focused on a far object.

(a) Rendered without blur estimation. (b) Rendered with blur estimation.

Figure 9: Results of merging a virtual object into a real scene image with defocusing. (Left column: rendering without blur estimation, rightcolumn: rendering with blur estimation. top row: focused on a near object, center row: focused on a marker, bottom row: focused on a farobject.)

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