SharpView: Improved Clarity of Defocused Content on Optical
See-Through Head-Mounted Displays
Kohei Oshima⇤ † Kenneth R Moser⇤ ‡ Damien Constantine Rompapas†
J. Edward Swan II‡††
Goshiro Yamamoto
Takafumi Taketomi
Christian Sandor†
Hirokazu Kato†† Interactive Media Design Laboratory

Nara Institute of Science and Technology(a)‡ Computer Science & Engineering

Mississippi State University

(b)

(c)

Sei Ikeda§§Mobile Computing Laboratory
Ritsumeikan University

(d)

(e)

Figure 1: The cause and effect of focus blur in Optical See-Through (OST) Head-Mounted Display (HMD) systems. (a) A user wearing the OST
HMD and related hardware used in our study. (b) Simplified schematic of an OST AR system. Blurring occurs when the virtual display screen
and real world imagery are viewed at unequal focal distances. (c), (d), (e): Views through an OST Augmented Reality system, where the realworld image (c) is in focus, causing the virtual image (d) to appear blurred; (e) an improved virtual image after application of SharpView.

A BSTRACT
Augmented Reality (AR) systems, which utilize optical see-throughhead-mounted displays, are becoming more common place, withseveral consumer level options already available, and the promise ofadditional, more advanced, devices on the horizon. A common factor among current generation optical see-through devices, though,is fixed focal distance to virtual content. While fixed focus is not aconcern for video see-through AR, since both virtual and real worldimagery are combined into a single image by the display, unequal
distances between real world objects and the virtual display screen
in optical see-through AR is unavoidable.
In this work, we investigate the issue of focus blur, in particular,
the blurring caused by simultaneously viewing virtual content andphysical objects in the environment at differing focal distances. We
additionally examine the application of dynamic sharpening filters
as a straight forward, system independent, means for mitigating thiseffect improving the clarity of defocused AR content. We assess theutility of this method, termed SharpView, by employing an adjustment experiment in which users actively apply varying amounts of
sharpening to reduce the perception of blur in AR content shown atfour focal disparity levels relative to real world imagery.
Our experimental results confirm that dynamic correction
schemes are required for adequately addressing the presence of blurin Optical See-Through AR. Furthermore, we validate the ability of
our SharpView model to improve the perceived visual clarity of focus blurred content, with optimal performance at focal differences
well suited for near field AR applications.
Index Terms:

H.5.1 [Information Interfaces and Presentation]:⇤ These authors contributed equally.

† e-mail: {oshima.kohei.of0, damien.rompapas.dk1, goshiro, takafumi-t,
sandor, kato} @is.naist.jp‡ e-mail: krm104@acm.org, swan@acm.org§ e-mail: ikeda.sei.jp@ieee.org

Multimedia Information Systems—Artificial, augmented, and virtual realities; I.4.4 [Image Processing and Computer Vision]:
Restoration—Wiener filtering
1

I NTRODUCTION

Optical See-Through (OST) Head-Mounted Displays (HMDs) haveseen an increase in both popularity and accessibility with the release of several consumer level options, including Google Glassand Epson Moverio BT-200, and announced future offerings, suchas Microsoft’s HoloLens, on the horizon. The transparent display
technology used in these HMDs affords a unique experience, allowing the user to view on-screen computer generated (CG) content
while maintaining a direct view of their environment, a property
extremely well suited for augmented reality (AR) systems. Unfortunately, the current generation of consumer-level OST HMDs
are only capable of presenting CG content at a single fixed focal
distance. This inherent limitation becomes problematic as the user
attempts to simultaneously view the world and CG objects together,
inducing focal rivalry as the eye’s optical system must continuously
adjust to accommodate both the real and virtual items.
Figure 1 (a) and (b) illustrate an OST AR system in which a user
concurrently observes real and CG images at disparate distances.
As the eye’s accommodation changes to match the focal distance ofthe real image, Figure 1 (c), the on-screen image appears blurred,Figure 1 (d). Naturally, the amount of blur perceived is directly related to accommodative ability, which varies from person to person
and undergoes further changes with age. The majority of individuals experience steady decline in accommodative ability between
child and adulthood [8], with more rapid decreases beginning toonset between 40–50, and general loss of sensitivity to focal differences occurring beyond 60 years of age. In order to effectively
address such a wide variance in focal ability across users, flexible
corrective measures, adaptable to specific user needs, are required.
Prior studies have proposed techniques for directly improving
the distortion effects caused by the optical combiners within OST
HMDs [16]. However, optical distortion from device specific components only contributes a constant error. Other prior work has applied image filtering and masking algorithms to video see-through(VST) systems to produce user-specific corrections [28], which allows users with abnormal and diminished vision to effectively view
imagery without the need for corrective lenses. A similar approach
has likewise been deployed in an OST system, with the aim of enhancing user vision [16]. However, all of these techniques use static
correction schemes, and therefore cannot address the blur produced
by OST focal rivalry. This blur does not remain constant, but continuously changes as the user fixates on objects throughout the environment. Therefore, addressing OST focal rivalry blur requires a
dynamic approach, which continually adjusts to match the changing
focal demand of the user.
The Point Spread Function (PSF) [20], used to measure the focal ability of a system, also has application for identifying the refractive power limit of the eye’s internal lens [32]. In this work,
we present an active focus blur correction method capable of producing dynamic image improvement by continuously updating a
PSF model for the user during system runtime. Our technique, designated SharpView, is the first methodology proposed for on-line
processing of virtual imagery to counteract focus blur in OST AR.
While it is possible to enhance the sharpness of all virtual objects
within sight, this might not always be preferable, particularly in applications where depth cues, due to blurring, are desired. Therefore,
the current scope of our SharpView model is to improve the clarity
of only the AR content under direct view in OST AR systems. We
believe this affords an immediate benefit to systems which employ
heads up displays (HUDs) or require the overlay of virtual objects
onto physical items of interest, such as in assembly or maintenancetasks. Likewise, we believe that dynamic adjustment of the perceived focus of virtual content may also aid in the reduction of eye
strain produced by the accommodative-convergence mismatch that
results in binocular stereoscopic systems.
In addition to the SharpView algorithm, we also present the results of a user experiment designed to quantify the perception of
focus blur in an OST AR system. We provide an analysis, which
concentrates on both the change in perceived blur at varying focal
disparities and the utility of SharpView for improving virtual image
clarity. We close with a final discussion of our experimental results
and comments to guide future work.
2

R ELATED W ORK

Fixed focal distance is an inherent limitation of current consumer
level HMD technology including not only VR but also VST andOST AR devices. While a large number of available options do
support 3D stereoscopic viewing, the inability to provide correct accommodative cues, to match the user’s changing convergence during use, decreases the realism and immersiveness of virtual content and increases the likelihood of simulator sickness and eye
strain [21]. Blur and other visual aberrations are also common
within single focus systems. Solutions for expanding the focal
range of HMDs have included not only contributions from hardware development but also the image processing domain as well.
2.1

Focus Correct HMDs

Investigations by MacKenzie et al. [26] have shown that a minimum of five focal planes will adequately produce an acceptable
range of accommodative cues to match user convergence. Whileusing only four focus levels, a stereoscopic multi-focal display developed by Love et al. [25] uses a high speed lens system to present
focused content at real-time response rates. Additional displays developed by Akeley et al. [2] and Schowengerdt and Seibel [34]utilize prisms and flexible membranes, respectively, to create variable focus image planes. While able to cover a much larger range,these systems too are only able to create a limited discrete numberof focus planes. Liu et al. [24] employ active liquid-lens technology to generate OST imagery over a continuous focal range, though
the response rate of the liquid-lens, 75ms, is too slow for use withdynamic virtual content. Light Field Displays (LFDs) have been
proposed, [23], as effective alternatives to lens based approaches
for generating accommodatively correct virtual content.
LFDs generate images by controlling both the color and direction of each light ray transmitted from the device into the user’seye [19]. Since the path of each ray is independently controlled, thefocal distance of virtual content is inherently variable. LFDs commonly employ microlens arrays for image generation, though thevolume of light rays required varies across implementation, with
Maimone et al. [27] claiming that ultra high-density displays are
required for effective near-eye LFD applications. Larger ray volumes however, produce greater processing requirements. Huang
et al. [15], for example, present a LFD which requires 20sec toresolve the Light Field projection equation of a single image. An
improvement on this earlier model though, [13], has produced a hybrid stereoscopic near-eye LFD comprised of layered LCD panelswith diffusers and focusing lenses. This system, however, is only
capable of producing VR, or VST AR, content and is not suitablefor OST applications. Much like lens based multi-focus systems,
the low performance, robustness, and availability to consumers haslargely limited the accessibility of current LFD technology, leading
to efforts in image processing for the development of more ubiquitous solutions to improving focus perception.
2.2

Focus Correction Through Software

The use of image processing is standard practice for rectifying defocus present in projector based systems caused by misalignment
between the projection and image plane. Zhang and Nayar [39]propose that such systems be modeled as a constrained optimization problem in order to minimize artifacts, while utilizing only theavailable dynamic range of the projector. Often though, a level ofblur is desired, to more accurately model depth of field effects, forexample. Brown et al. [5] were able to successfully extend the depthof field in their projector based system by applying a pre-filter to theprojected image. Daisuke et al. [18] also utilize pre-filtering methods to extend depth of field in conjunction with pre-calculated PSF
values obtained by vibrating the projector lens at high speeds. Prefiltering for focus manipulation has also seen application outside of
projector systems.
Alonso and Barreto [3] as well as Yellott and Yellott [38] utilizepre-filtering to generate on-screen images intended to be viewed
by visually impaired individuals. Their methodologies employ a
pre-calculated PSF function to perform a deconvolution operationin order to distort the image within the rendering pipeline before
it is finally presented on the display. Correctly modeling the PSF,
to match deficiencies in the user’s vision, enables the pre-distortion
step to produce images which appear perceptually correct to theuser. Subsequent improvements to this technique, by Huang etal. [14], reduce the low contrast side-effect of pre-filtering by presenting images on multi-layer displays. Pamplona et al. [31] further
extend the application of these techniques to produce a VST HMD
system able to replace corrective lenses for visually impaired users.
A study by Montalto et al. [29] applies the same user specific predistortion method in order to render visually correct images to OST
devices. Similar work by Itoh et al. [17] applies pre-filtered image masks, rendered to OST displays, to reduce blur and improveuser vision. These methods, though, ultimately utilize only static
correction filters to address constant systematic effects and vision
irregularities. We have developed a novel approach, and first attempt, to diminish the visual presence of OST AR focus blur by
applying a dynamic PSF model, which modulates the amount ofcorrection provided throughout the user experience.
3

S HARP V IEW

As light from the environment passes through the eye, the rays arebent by the cornea and internal lens to ideally focus onto the retina.

The diffraction pattern of the light during this process is modeled by
a PSF. If defects are present in the physical structures of the eye, as
would be the case in individuals with inhibited vision, a static PSF
is sufficient for determining the optical power requirements necessary for corrective lenses able to adjust or redirect the light rays
back through the proper focal point within the eye. This same procedure can be analogously expressed, within the image processing
domain, as a deconvolution operation between the image focusedon the retina and the PSF. Therefore, if a PSF is known in advance,
it is possible to restore a degraded image through deconvolution.
Common deconvolution operations include inverse filtering,
Wiener filtering, and the iterative Richardson-Lucy algorithm [11].
Difficulty in performing deconvolution with a PSF arises, however, due to inaccuracies in the diffraction pattern model. Okumura
et al. [30] have utilized information from captured images of 2D
markers to estimate the PSF of a camera based system. Unfortunately, the same principles are not applicable to the human eye,since only the user themselves have access to the perceived image. Additionally, techniques used to measure a user’s PSF require
expensive or large physical hardware, such as auto-refractometers,
and a processing demand which is not suited for real time applications [32]. Our SharpView algorithm overcomes these limitations
by employing a simplified PSF model based on three user and system specific parameters: pupil diameter, distance to real world gazepoint, and focal distance of the HMD display screen.
3.1

Image Convolution

In general, the convolution of an image o with a point spread function p produces a blurred image b.
b = o⇤ p(1)

The symbol ⇤ represents the convolution operation. Equation (1)can be expressed within the spatial frequency domain as
B = O·P(2)

where B, P and O represent Fourier transforms of b, p and o, respectively. A blurred image can, likewise, be corrected throughapplication of the inverse PSF, O0 = B/P. However, the following Wiener Deconvolution is optimal for performing this task withminimal noise.
B·P
O0 =
(3)
|P|2 + |C|2
Here, P represents the complex conjugate of P. C is a constantto prevent instability of the solution when values of P are small.
We apply this same principle to OST AR in order to create a final
sharpened on-screen image with improved visibility over the original. We accomplish this by applying the Wiener filter, adjusted
using the estimated PSF of the user, to rendered images O and display the resulting sharpened image S to the HMD.
S=
3.2

O·P
|P|2 + |C|2

points on the retina. The intensity distribution, p, can be represented by the following function.
P(x, y) =1x2 + y2
exp(
)
2
2ps
2s 2

(5)

In equation (5), s represents the focal blur size, while x and y represent the pixel position of the emanating screen point. Our simplified PSF model, therefore, requires only one parameter, s , to be
determined at run time.
Consider the simple system, shown in Figure 2, used to model
the combined optical system of the eye and OST HMD screen. The
center lens, in this system, represents the internal crystalline lensof the eye and the right imaging plane characterizes the retina. As
the user’s gaze fixates at point M in the real environment, the lighttraveling from this point creates an image at point m on the eye’s
imaging plane, after passing through the lens. Similarly, light emitted from point M 0 on the HMD screen is imaged at point m0 on theretina. While the user’s focus is centered on the real environment,
all of the light rays from the display will be improperly focused by
the lens causing on-screen content to appear blurred. The radius of
this blur is equal to s in our Gaussian function, equation (5). Thiss can be derived from the basic triangle ratio and lens equations asfollows:
av 1 1s= ( 0)(6)
2 u
u
Once obtained, it is then necessary to scale from the eye’s image
plane back to the virtual display. If the radius of display blur isexpressed as sd , the ratio between the eye’s image plane and screen
is expressed as follows.
s : sd = v : u0(7)Here, sd is directly obtainable from equations (6) and (7),asd = (1
2

u0
)
u

(8)

where a is pupil diameter, u is distance from the eye to the realworld gaze point, and u0 represents the distance from eye to HMD
image plane. When performing the actual convolution between thefilter and screen image, generally, sd may be converted to pixel
size, scaling the values based on the dot matrix of the specific display used. Our complete SharpView algorithm simplifies blur correction into a single convolution operation, where the filter is constructed from an estimated PSF, requiring only three parameters, allmeasurable at runtime.
Further simplification might be considered by presuming a constant pupil size during usage. However, numerous studies [12, 1,
35, 33] have shown that pupil size is not only dependent on lighting
but also mental load and task concentration, making live measurement of the pupil advisable. Additional system complexity may(4)

PSF Estimation

Effectively counteracting dynamic focus blur, caused by accommodative differences between the display screen and world in OST
HMDs, requires the PSF to be determined at run-time. We accomplish this by using a simplified Gaussian function to approximate
the PSF, which allows for faster update rates but with a reduction in
accuracy.
We generate our approximation by modeling the intensity of thelight rays, emitted from the display screen, intersecting varying

Figure 2: Optical system formed by the user’s eye and an OST HMD.
The imaging plane corresponds to the user’s retina and the lens aperture to the user’s pupil.

(a)

(b)

Figure 3: The OST HMD system used in our experiment. (a) An example subject wearing the HMD and eye tracking combination. The
right eye-piece is covered to produce a monocular view. (b) Closer
view of the attached camera used for tracking subject gaze and measuring pupil size.

arise from the necessity to record the fixation distance between theuser’s eye and real object under focus. While it is possible to calculate the user’s fixation through on-line vergence angle measurements provided by stereoscopic eye-tracking [7, 9, 37], an alternative method leveraging known 3D environmental layout information, compiled off-line using a depth sensor or camera such as theMicrosoft Kinect, may be more suited for current consumer systems. Correlating the intersection of the user’s gaze with this prerendered environment model, would facilitate rough estimation forthe current focal demand of the user.
4 E XPERIMENTAL D ESIGNIn order to develop an effective improvement strategy for correcting focus blur in OST AR, measures for the amount of blur perceived by users must be obtained. Since focus blur occurs duringaccommodation rivalry between physical and on-screen content, it
is essential to define the level of awareness users have to the presence of blur in rendered images. We developed SharpView based on
the premise that perceived focus blur varies according to the focal
disparity between the display and world. Therefore, if a physicalobject is viewed at a distance approximately equal to the focal distance of the HMD, very little focus blur should occur. However, if
the distance to the physical object greatly differs from the HMD focal distance, a large amount of focus blur should be observed. Thisimplies that a static correction scheme will not suffice, but rather adynamic range of sharpening levels is required.
Based on this presumption, we designed and implemented a user
study investigation to quantify user perception of focus blur within
an OST AR system. Our experimental hypotheses are two-fold.
First, we believe preferential sharpening levels for each subject willvary according to the magnitude of focal disparity, with more sharpening desired at greater disparities, and very low sharpening degrees chosen for the low focal disparity conditions. Secondly, we
anticipate the users to select sharpening values which will follow asimilar trend as that predicted by our SharpView model over-all.
4.1 Subjects
13 participants (10 male, 3 female), between the ages of 22 and33, participated in our study. All subjects were university students
recruited through email solicitation. Each subject was required toprovide written consent before proceeding with the study and wasprovided monetary compensation for their time. 8 of the 13 subjects stated that they possessed normal uncorrected vision. The remaining 5 required the use of corrective contact lenses, which were
worn throughout the duration of the task. Subjects were provided athorough explanation of the experimental hardware and procedure
before beginning any aspect of the experiment.
4.2 Experiment Platform
An Epson Moverio BT-200 OST HMD is used as the primary display worn by each subject. The Moverio is a binocular display

(a)

(b)

Figure 4: Views of the eye tracking calibration process, as seen
through the HMD. (a) An on-screen cross-hair used during the gazeto-screen mapping phase of calibration. (b) Correct placement of the
red square during the position adjustment phase of system calibration.

(a)

(b)

Figure 5: Location of subjects relative to reference images placed at
25 cm (a) and 500 cm (b) from the subjects’ eyes.

capable of presenting stereo imagery at 60Hz with a maximum resolution of 960 ⇥ 540 per eye. The focal distance of the display wasboth independently measured and confirmed by the manufacturer
to be approximately 7m. In order to remove effects due to stereoscopy and binocular rivalry, the right eye piece of the display iscovered with a patch of black opaque cloth, Figure 3 (a). We attached a Microsoft LifeCam HD-6000 webcam, set to a resolutionof 1280 ⇥ 720 at 30fps, beneath the left eye piece, Figure 3 (b). The
camera itself is modified by removing the infrared and adding a visible light filter along with a pair of IR LEDs. Measurements for theleft eye’s pupil diameter, as well as gaze direction, are made using
images captured by the HD-6000 and processed via version 0.4.5
of the PupilLabs open-source eye tracking software [22]. Accuracy of the gaze tracking was confirmed to within 1 with measures
recorded at the frame rate limitation of the cameras, 30Hz. The
OpenCV image processing library is used to perform the deconvolution required by SharpView and generate the on-screen images
displayed at run time. A wireless hand held controller is additionally used to record subject adjustments and responses during all
calibration and experimental tasks.
4.3 Eye Tracking
In order to induce and maintain the presence of focus blur during
our experimental task, subjects must be forced to fixate their gaze
onto physical objects in the world and also be prevented from shifting their focus onto on-screen content. We incorporate an eye tracking process into our OST HMD system making it possible to determine the subject’s gaze direction and impose countermeasures toprevent accommodation change when their gaze moves toward AR
imagery. A brief calibration process is required, though, in order tomap the subject’s gaze onto screen coordinates of the OST display.
We actually employ a two phase procedure to both calibrate our
eye-tracking system as well as adjust the placement of AR content
within the subject’s field of view.
First, we determine the direction of the subject’s gaze with relation to the HMD screen. This process is accomplished through astandard gaze mapping routine, commonly used to calibrate commercial and consumer eye-tracking systems. A set of 12 crosshairs, positioned to uniformly cover the on-screen area of the HMD,

(a)

(b)

(c)

Figure 6: View through the HMD of a reference and sharpened virtual image pair during the adjustment task. The rendered gauge provides anindication of the amount of sharpening correction applied. (a) Low, (b) moderate, and (c) high amounts of sharpening. Though color and contrast
appear to differ, both on-screen and reference images used during the experiment were black and white. The images shown are captured
through a digital camera and do not necessarily reflect the actual visual appearance experienced by the subjects during use.

are presented one at a time to the subject, Figure 4 (a). For eachcross-hair, the subject is instructed to fixate and maintain their gazeon the center of the cross for approximately 5 seconds. During
this fixation period, the gaze angle, in relation to the eye-tracking
camera, is recorded. Using the known screen locations of eachcross-hair and the recorded gaze angle measurements, a mapping
between gaze direction and screen pixels is determined. The PupilLabs open-source software provides built-in routines for both collecting the gaze angle data and performing the mapping calculations.
The second phase of calibration is intended to properly position
the AR content within the subject’s field of view. We perform thisoperation by rendering a red square to the HMD screen and allowing subjects to manually adjust its location. Using a hand held controller, subjects are allowed to move the square up and down andside-to-side until the right edge of the square is aligned flush withthe left edge of a real reference image placed in front of the display,
Figure 4 (b). Proper vertical positioning occurs when the center ofthe square aligns with the center of the reference image.
After successful calibration, We are able to track the subjects’gaze across the HMD screen and detect when fixation is moved toward on-screen content. We simply mute, turn off, all graphics asa counter measure preventing subjects from focusing onto the virtual screen. Once gaze is removed from the AR content’s location,
graphics are restored and the experiment continued.
In addition to gaze information, the eye-tracking software also
provided a rough measure of pupil diameter in terms of image pixels. Presuming a standard iris diameter of 12mm [6, 10], we wereable to calculate the distance scale between camera pixels of the eye
tracking camera and physical distance. Using this same scale, redetermined per subject at the start of the experiment, the approximate
pupil diameter in mm for each subject is estimated at run-time.
4.4

Adjustment Task

We utilize a simple adjustment task to obtain measures for theamount of perceived focus blur and preferred level of sharpening
correction for subjects viewing real and virtual content through our
OST AR system. Focus blur is induced by making subjects fixate
their gaze upon real reference images, placed in front of the display,at distances differing from the actual focal distance of the HMD. A
virtual image, identical to the real reference image, is then rendered
to the display screen. The subject will consequently perceive therendered image as blurred, even though it is unaltered, due to the
focal disparity between the reference image and HMD.

(a)

(b)

(c)

(d)

Figure 7: The four black and white stone patters used as both reference and on-screen imagery during the adjustment task.

The images used in our experiment were chosen to provide large
amounts of high frequency visual information, such as clearly defined edges for example. Four asymmetric stone patterns wereselected for use during the experiment itself, Figure 7, with anadditional four images used during training sets before data wasrecorded. The resolution of the images displayed to the HMD,
as well as the size of the reference images, was adjusted so thatboth images subtended a constant 6 of visual angle. In order tominimize effects from color difference between the printed and onscreen images, both the HMD and reference photos were kept black
and white.
We presented the reference images at four distance levels, 25cm,
50cm, 100cm, and 500cm, as measured from the subject’s eye.
These distances were selected in order to obtain measures over a

675

650

600

200

Table 1: Mean and standard deviation for user-s and SharpView-s
values, expressed in terms of visual angle, at each focal difference
level.

●●●●●

σ°, ± 1 SEM

1.5

●

●

●

●
●

●

1.0

●
●

●

●

0.5

●

●

●

●
●

0.0
user sharpview

user sharpview

user sharpview

SharpView-s

mean ( )stdmean ( )

std

675

0.89

0.37

1.14

0.21

650

0.60

0.32

0.61

0.12

600

0.51

0.12

0.27

0.05

200

0.22

0.15

0.02

0.002

●

●
●

user-s

Focal Difference (cm)user sharpview

σ category

Figure 8: User-s and SharpView-s , expressed as visual angle,
grouped by focal disparity. The mean and standard error of the mean
(SEM) are shown in blue.

plausible viewing range for an indoor AR application. The 50cm
condition is intended to roughly mimic arms-length. As previously
noted, the accommodative ability of individuals, particularly within
the near filed, greatly diminishes with age. Therefore, we chose
our minimum distance of 25cm to provide a closer accommodative
distance which still falls within the visible limits for older subjects
that might suffer from degenerate focal ability. The 100cm condition continues the doubling trend between the 25cm and 50cm
condition. Due to space limitation within our experimental environment, it was not possible to acquire an indoor space able to matchthe 7m focal distance of the Epson Moverio BT-200. Therefore,
500cm, 5m, was chosen to match, as closely as possible, the control condition where HMD and real world object focal distance areapproximately equivalent. Figure 5 shows the location of reference
images relative to subjects at the 25cm and 500cm distance levels.
In order to increase the applicability of our measures across
HMD and system types, we henceforth express our four reference
distance levels as the corresponding difference between the reference and on-screen image focal distance. Therefore, using the 7m
fixed focal distance of the HMD screen, the 25cm reference image
distance produces a focal difference of 675cm. The 50cm, 100cm,
and 500cm reference images produce focal difference of 650cm,
600cm, and 200cm, respectively. These values represent the changein accommodative demand required by subjects to change their focus from one image to the other, with a lower focal demand corresponding to less focus blur.
The degree to which each subject perceives blurring of the virtual image is measured by applying our SharpView algorithm andallowing the subject to manually adjust the s , or radius of sharpening, until the on-screen image appears the most visually clear, or
sharp, compared to the reference image. We allowed subjects toincrement or decrement s to values between 1 and 40 pixels, at 1
pixel increments. The starting sharpness is randomly selected at the
beginning of each trial. An additional gauge, rendered on-screen
beneath the reference image, provides subjects with an indicationof where, within the 1–40 pixel range, the current selected valuelies. The intention of the gauge is to assist users in their selection
by allowing them to first narrow the range of beneficial s ’s among
three zones, low, medium, or high, and then fine tune their final
selection of an optimal s relative to that primary grouping. Eachgauge zone reflects a 13 pixel change in s . Figure 6 provides aview through the HMD of the task visuals, as seen by each subject.
The visual differences in sharpness between the virtual image at svalues in each of the three gauge zones is also shown.

4.5

Procedure

Before beginning any trial or training sets, we first fit the subject
with our OST HMD and eye-tracker combination, and verified thatthe attached camera had a clear, unobstructed view of the subject’s
left eye. The eye-tracking calibration, as previously described, wasthen performed and the accuracy of gaze measurements verified using the PupilLabs software. A brief training set was also conductedprior to beginning any data recording, in order to verify that the subject had a clear understanding of how to both identify the presenceof focus blur, and to properly perform the s adjustment.
During each trial of the experiment, a reference image was firstplaced in front of the display, at one of the 4 distance levels. The
subject was then instructed to fixate their gaze onto the reference
image, after which the identical virtual image was rendered to thedisplay with a randomly selected s , between 1–40 pixels, used toapply initial sharpening. We employed the previously described eye
tracking countermeasures to prevent the subject’s gaze from drifting
onto the virtual image. Subjects were then verbally instructed to adjust s to first determine which of the three gauge zones provided thebest improvement, and then to further refine their choice within thatzone until they determined the s which provided optimal clarity. A
wireless hand-held controller was used by the subject to performthe s adjustment, with one button used to increase and a second to
decrease values. A third button was used to confirm the subject’s
final response. Once the subject had confirmed their response, theselected user-s value, along with a SharpView-s calculated using
the live measurement of pupil diameter, was recorded.
A total of 8 trials, two for each of the four stone images, were
performed at each distance level. After all 8 trials were completed,the process was repeated for the same images displayed at one of
the remaining distance levels. Both the distance level and image
presentation ordering were permuted to ensure that no two subjects
experienced the same sequence of reference images or distance levels. The 8 trials, at each of the 4 distance levels, produced a total of
32 s measurements per subject.
5

A NALYSIS OF R ESULTS

We recorded both the subject selected s , which we will denote asuser-s , and the s calculated from equation 8 using the on-line pupil
measurements and distances to the reference and virtual images,
which we will denote as SharpView-s . Since both s values represent the radius of perceived blur, we convert the units from pixels into degrees of visual angle, based on the resolution, field ofview, and 7m focal distance of the display, which results in 1 pixel
subtending approximately 0.043 of visual angle. Additionally, as
previously noted, we denote data collected at each of the four reference image distances using the difference in focal distance between
the images and the HMD screen, with the 25cm, 50cm, 100cm, and500cm reference image distances corresponding to 675cm, 650cm,
600cm, and 200cm in focal disparity respectively.

0.0
2.0
1.5

s1

sharpview−σ°

1.0

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s2

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y= 0.56x + 0.17 r 2= 0.24 y= 0.74x + 0.15 r 2= 0.68 y= 2.08x − 0.04 r 2= 0.50 y= 1.47x − 0.17 r 2= 0.50 y= 1.63x − 0.35 r 2= 0.79 y= 2.45x − 0.57 r 2= 0.86 y= 0.97x − 0.14 r 2= 0.99
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s9

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s12

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y= 4.78x − 0.53 r 2= 0.85 y= 0.89x + 0.02 r 2= 0.10 y= 2.08x − 0.81 r 2= 1.00 y= 1.22x − 0.27 r 2= 0.97 y= 1.21x − 0.30 r 2= 0.85 y= 0.77x − 0.12 r 2= 0.82
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focal disp. (cm)
675
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200

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1.5

user−σ°Figure 9: Comparison between user-selected sigma and the sigma predicted by our SharpView algorithm; both values are expressed as degrees
of visual angle. The data points are color-coded according to focal disparity. The regression line is fit to the centroid (filled diamonds) of eachdistance. Ideal correspondence between our SharpView model and user preference is represented by a dashed line along the diagonal.

s1 − s132

y= 1.77x − 0.46 r = 0.95

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Figure 10: Data from Figure 9, averaged over all users. Ideal correspondence between our SharpView model and user preference isrepresented by a dashed line along the diagonal.

Figure 8 provides a side by side comparison of the user-s selections and the modeled SharpView-s . Visual inspection reveals
a noticeable trend of increasing user-s values with increasing focal
distance disparity. The mean and standard error of the mean for
the four user-s groupings are also shown in Figure 8. Repeatedmeasures analysis of variance (ANOVA) across the user-s groupings, confirms that user-s significantly differs according to distance(F(3, 36) = 16.7, p < 0.001). A Ryan REGWQ post-hoc homogeneous subset test additionally indicates that all means are significantly different at p  0.05.
The modeled SharpView-s , likewise, increases with larger focal
disparity distance, though at a slightly higher rate than the subject
selected values. Paired ANOVA testing across the separate user
and SharpView-s groupings at each disparity level shows that the

subject-chosen user-s values do significantly differ from the modeled SharpView-s at the 200cm, 600cm, and 675cm focus disparity levels (F(1, 12) = 39.2, p < 0.001, F(1, 12) = 22.35, p < 0.001,
and F(1, 12) = 4.705, p ⇡ 0.051, respectively). However no significance was found between user and SharpView-s at a disparity of
650cm (F < 1).
We obtain a second metric for quantifying the modeling accuracyof our SharpView algorithm by plotting user-s versus SharpViews . Figure 9 provides a per-subject s comparison, while Figure 10 shows the comparison with all data combined. While boththe SharpView model and accommodative functions of the user
are both exponential in nature, our correspondence plots are intended to visualize the correlation between our sharpening prediction and user preference. Therefore, an ideal modeling between
our SharpView and user selections will result in a linear trend, expressed in each plot as a dashed line along the diagonal.
The regression lines, shown in black, are fit to the centroids of thedata clusters associated with recordings from each of the four focal
disparity levels. Inspection of per-subject comparisons, once again,
indicates that the modeled SharpView-s values increase at a larger
rate than the subject-selected s values. The complete data comparison, Figure 10, confirms that this is indeed the case. However,
it is clearly evident that the modeled SharpView-s is most accurate at the 650cm focus disparity level, matching the ANOVA findings. Additionally, the clustering exhibited by data points withineach distance level also reveals that subjects had the most consistent agreement on s choices at the 200cm, 600cm, and 650cm disparities. Mean and standard deviation values for both the user andSharpView-s results at each distance level are provided in Table 1.
6

D ISCUSSION

The impact of our experimental results is two-fold. Foremost, thesignificant difference in user-s values across focus disparities, as
exhibited in Figure 8, confirms that subjects are not only able toperceive the effects of focus blur on AR content, but also the improvement in clarity afforded by sharpening. Secondly, the significant differences in user selected correction validates our claimthat focus blur mitigation requires a dynamic solution, such as our
SharpView algorithm.
Since the focal disparity is lowest at the 500cm reference image distance—200cm focal difference—we would also expect theeffects from focus blur to be lowest during trials at this level. Conversely, as the disparity rises, the amount of focus blur perceived inAR content should also increase. This is indeed the trend we see inthe user-s results. We are able to, likewise, verify the positive impact of sharpening using similar reasoning. If the use of sharpening
did not afford any improvement, or noticeable change in image appearance, we would expect to see overlapping s distributions. Thisis, of course, not the case for our results (Figure 8), but instead our
statistical analysis shows that mean user-s values, between disparity levels, are not equivalent, but rather follow a similar trend to thatpredicted by our SharpView model.
Both the visual inspection and statistical analysis of our calculated SharpView-s values show that the SharpView model is able
to most accurately produce sharpening correction for focus blur at
a focus disparity level of 650cm. For our OST HMD system, which
has a focal distance of 700cm, this means that SharpView performs
optimally when subjects view AR content in relation to real objects that are located at approximately 50cm, or arms length, away.
This means that applications requiring the use of AR for near field,
or near vision, tasks, such as maintenance, 3D modeling, or medical procedures, will experience the largest benefit from SharpView
compensation.
The individual s correlation plots, Figure 9, show that optimalblur correction is quite subject specific. It is not surprising, however, that this is the case. Our SharpView model is intended to
be a lightweight solution, and subsequently uses a very simplified
PSF model, which ignores user specific criteria. We do not, forexample, consider influences from existing vision impairments, including myopia or presbyopia. The simplified PSF employed by
SharpView, instead, presumes an ideal system, allowing us to reduce the number of independent parameters to three, pupil diameterof the user, focal distance to the display screen, and focal distanceto world objects.
A pre-experiment questionnaire, provided to each subject prior
to performing the experimental task, did record a base level of subject specified visual acuity. S1, S3, S6, S9, and S10 all required
the use of corrective contact lenses. S2 and S4 described theirvision as better than 20/20, with the remaining subjects claiming
normal 20/20 visual acuity. Since we did not conduct any formal
optometrical evaluations of the subjects though, we are not able toaccurately investigate the possible impact of visual impairment, or
heightened acuity, present in multiple participants. We would encourage future investigations to consider including simple vision
tests, using Snellen or LogMAR charts, which may identify visionrelated trends and aid in the development of correction factors for
SharpView, and similar methods, based on known vision requirements. Furthermore, the s values from Figures 9 and 10 also do not
visually show the impact of pupil diameter across trials or subjects.
While the distance between the HMD screen and reference image
target conditions was held constant between subjects, the pupil diameter between participants was not controllable. Of course, variation in pupil diameter alone is not sufficient to explain or model
the variability across subjects, it is still important to remember thisfactor when considering the sharpening responses.
An additional improvement to our experiment methodology includes expanding the number of disparity levels examined, or perhaps utilizing depth sensors or cameras to directly measure the distance to the reference images, or objects. The promised availabilityof such hardware in forthcoming consumer OST HMDs, such as theMicrosoft HoloLens, not only makes the implementation of such a
procedure more accessible to the research community at large, butalso showcases the versatility and ease to which our SharpView algorithm can be adapted across display systems.
7

C ONCLUSIONS AND F UTURE W ORK

In this work, we have presented a method for mitigating the effects
of focus blur, caused by unequal accommodative demand, inherent
to current consumer OST displays. Our SharpView technique uti-

lizes a simplified PSF model of the human visual system, whichenables real-time generation of sharpening filters, able to mitigate
the dynamic visually degrading effects from focal disparity. We
also performed a formal user investigation to confirm the utility ofimage enhancement methods for improving the clarity of blurred
AR content.
Our experimental results confirm that users of OST AR are able
to not only perceive the presence of focus blur, but also the improvements afforded by sharpening on-screen imagery. In addition, theoptimal amount of blur reduction desired by subjects varied acrossfocal disparity levels, verifying the need for dynamic blur reduction solutions. We were also able to validate the effectiveness of
SharpView in mitigating the presence of blur at focus differences
optimally suited for near field AR systems and applications.
However, further work is required. In particular, studies examining the relationship between visual acuity and blur perception could
identify the correction factors needed to improve the robustnessof SharpView and generalize its utility across a larger user base.
Subject specific visual acuity data, specifically, will be beneficial
in modeling the complete ideal PSF, providing additional insight
into the accuracy of our own simplified SharpView and its ability toproperly predict sharpening correction on a per user basis. Nevertheless, we believe that the SharpView methodology will be readilyextendable to forthcoming consumer OST devices. A direct benefit from these next generation HMDs is the utilization of on-board
depth sensors and cameras to provide real-time measurements ofthe focal distance to world objects, allowing the active correction of
focus blur over a continuous range of object distances, as opposed
to a discrete number of focal levels as employed by our study.
Furthermore, subsequent investigations extending SharpView tostereoscopic applications will be able to quantify the possible benefits in eye strain reduction due to alleviation of accommodationconvergence mis-match from binocular viewing. The identificationof currently unknown side effects, positive or negative, must also
be considered, along with the limitations of sharpening correction
in systems where focus blur may be desirable to provide essential
depth cues of virtual objects.
Finally, we believe that our results are highly relevant for a wide
range of 3D user interfaces. First, enabling users to perceive graphics more clearly improves performance for all universal interaction
tasks [4], including selection, manipulation, travel, way-finding,system control, and symbolic input. Second, our results can also
be applied to displays other than OST HMDs, as long as there isa disparity between image plane and user focus. For example, tohand-held AR, as demonstrated by Tomioka [36] and colleagues,
or projective displays, as demonstrated by Zhang and Nayar [39];both systems could be enhanced based on our results.
ACKNOWLEDGEMENTS
Support for this work is provided in part by NSF awards IIS1320909 and IIS-1018413 to J. E. Swan II, and a NASA Mississippi
Space Grant Consortium fellowship, Bagley Graduate Internshipand JSPS scholarships to K. Moser, as well as the Japan Society for
the Promotion of Science under awards Grant-in-Aid for Scientific
Research (B) #15H02737 and Challenging Exploratory Research#15K12084.
R EFERENCES[1] S. Ahern and J. Beatty.
Pupillary responses during information processing vary with scholastic aptitude test scores. Science,
205(4412):1289–1292, 1979.
[2] K. Akeley, S. J. Watt, A. R. Girshick, and M. S. Banks. A stereo
display prototype with multiple focal distances. In ACM Transactions
on Graphics (TOG), pages 804–813. ACM, 2004.
[3] J. Alonso, M. and A. Barreto. Pre-compensation for high-order aberrations of the human eye using on-screen image deconvolution. In

Engineering in Medicine and Biology Society, volume 1, pages 556–
559, 2003.
[4] D. Bowman, E. Kruijff, J. LaViola Jr., and I. Poupyrev. 3D User
Interfaces Theory and Practice. Addison Wesley, 2004.
[5] M. S. Brown, P. Song, and T.-J. Cham. Image pre-conditioning forout-of-focus projector blur. In Computer Vision and Pattern Recognition, volume 2, pages 1956–1963, 2006.
[6] P. Caroline and M. Andre. The effect of corneal diameter on soft lensfitting, part 2. Contact Lens Spectrum, 17(5):56–56, 2002.
[7] B. C. Daugherty, A. T. Duchowski, D. H. House, and C. Ramasamy.
Measuring vergence over stereoscopic video with a remote eye tracker.
In Proceedings of the 2010 Symposium on Eye-Tracking Research &Applications, pages 97–100. ACM, 2010.
[8] A. Duane. Studies in monocular and binocular accommodation with
their clinical applications. American Journal of Ophthalmology,
5(11):865–877, 1922.
[9] A. T. Duchowski, D. H. House, J. Gestring, R. Congdon, L. Świrski,
N. A. Dodgson, K. Krejtz, and I. Krejtz. Comparing estimated gaze
depth in virtual and physical environments. In Proceedings of the
Symposium on Eye Tracking Research and Applications, pages 103–110. ACM, 2014.
[10] J. V. Forrester, A. D. Dick, P. G. McMenamin, F. Roberts, and E. Pearlman. The eye: basic sciences in practice. Elsevier Health Sciences,
2015.
[11] R. C. Gonzalez and R. E. Woods. Instructor’s Manual for Digital
Image Processing. Addison-Wesley, 1992.
[12] E. H. Hess and J. M. Polt. Pupil size in relation to mental activity during simple problem-solving. Science, 143(3611):1190–1192, 1964.
[13] D. C. Huang, K. Chen, and G. Wetzstein. The light field stereoscope:
immersive computer graphics via factored near-eye light field displayswith focus cues. ACM Transactions on Graphics (TOG), 34(4):60,
2015.
[14] F.-C. Huang, D. Lanman, B. A. Barsky, and R. Raskar. Correcting for
optical aberrations using multilayer displays. ACM Transactions on
Graphics (TOG), 31(6):185, 2012.
[15] F.-C. Huang, G. Wetzstein, B. A. Barsky, and R. Raskar. Eyeglassesfree display: towards correcting visual aberrations with computational
light field displays. ACM Transactions on Graphics (TOG), 33(4):59,
2014.
[16] Y. Itoh and G. Klinker. Light-field correction for spatial calibration of
optical see-through head-mounted displays. Visualization and Computer Graphics, IEEE Transactions on, 21(4):471–480, 2015.
[17] Y. Itoh and G. Klinker. Vision enhancement: defocus correction via
optical see-through head-mounted displays. In Proceedings of the 6th
Augmented Human International Conference, pages 1–8. ACM, 2015.
[18] D. Iwai, S. Mihara, and K. Sato. Extended depth-of-field projector by
fast focal sweep projection. IEEE Transactions on Visualization andComputer Graphics (TVCG), 21(4):462–470, 2015.
[19] A. Jones, I. McDowall, H. Yamada, M. Bolas, and P. Debevec. Aninteractive 360 light field display. In ACM Special Interest Group on
Computer GRAPHics (SIGGRAPH), page 13, 2007.
[20] R. C. Jones. On the point and line spread functions of photographic
images. JOSA, 48(12):934–937, 1958.
[21] D. Kahneman and J. Beatty. Pupil diameter and load on memory.
Science, 154(3756):1583–1585, 1966.
[22] M. Kassner, W. Patera, and A. Bulling. Pupil: an open source platform for pervasive eye tracking and mobile gaze-based interaction. In
Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, pages 1151–
1160. ACM, 2014.
[23] D. Lanman and D. Luebke. Near-eye light field displays. ACM Transactions on Graphics (TOG), 32(6):220, 2013.
[24] S. Liu, D. Cheng, and H. Hua. An optical see-through head mounted
display with addressable focal planes. In International Symposium on
Mixed and Augmented Reality (ISMAR), pages 33–142. IEEE, 2008.
[25] G. D. Love, D. M. Hoffman, P. J. Hands, J. Gao, A. K. Kirby, and
M. S. Banks. High-speed switchable lens enables the development of a
volumetric stereoscopic display. OSA Optics Express, 17(18):15716–
15725, 2009.
[26] K. J. MacKenzie, D. M. Hoffman, and S. J. Watt. Accomodation tomultiple focal plane displays: Implications for improving stereoscopic
displays and for accommodation control. Journal of Vision, 10(8):22,
2010.
[27] A. Maimone and H. Fuchs. Computational augmented reality eyeglasses. In IEEE International Symposium on Mixed and Augmented
Reality (ISMAR), pages 29–38, 2013.
[28] C. Montalto, I. Garcia-Dorado, D. Aliaga, M. M. Oliveira, and
F. Meng. A total variation approach for customizing imagery to improve visual acuity. ACM Transactions on Graphics (TOG), 34(3):28,
2015.
[29] C. Montalto, I. Garcia-Dorado, D. Aliaga, M. M. Oliveira, and
F. Meng. A total variation approach for customizing imagery to
improve visual acuity. ACM Transactions on Graphics (TOG),
34(3):28:1–28:16, 2015.
[30] B. Okumura, M. Kanbara, and N. Yokoya. Augmented reality basedon estimation of defocusing and motion blurring from captured images. In IEEE International Symposium on Mixed and Augmented
Reality (ISMAR), pages 219–225, 2006.
[31] V. F. Pamplona, M. M. Oliveira, D. G. Aliaga, and R. Raskar. Tailoreddisplays to compensate for visual aberrations. ACM Transactions on
Graphics (TOG), 31(4):81, 2012.
[32] J. Santamarı́a, P. Artal, and J. Bescós. Determination of the pointspread function of human eyes using a hybrid optical–digital method.
JOSA A, 4(6):1109–1114, 1987.
[33] T. Schaefer Jr, J. B. Ferguson, J. A. Klein, and E. B. Rawson. Pupillary
responses during mental activities. Psychonomic Science, 12(4):137–
138, 1968.
[34] B. T. Schowengerdt and E. J. Seibel. True 3-d scanned voxel displays
using single or multiple light sources. Journal of the Society for Information Display, 14(2):135–143, 2006.
[35] H. Simpson and S. M. Hale. Pupillary changes during a decisionmaking task. Perceptual and Motor Skills, 29(2):495–498, 1969.
[36] M. Tomioka, S. Ikeda, and K. Sato. Approximated user-perspective
rendering in tablet-based augmented reality. In International Symposium on Mixed and Augmented Reality (ISMAR), pages 21–28. IEEE,
2013.
[37] R. Wang, B. Pelfrey, A. T. Duchowski, D. H. House, et al. Online
gaze disparity via bioncular eye tracking on stereoscopic displays. In
3D Imaging, Modeling, Processing, Visualization and Transmission(3DIMPVT), 2012 Second International Conference on, pages 184–
191. IEEE, 2012.
[38] J. I. Yellott and J. W. Yellott. Correcting spurious resolution in defocused images. In Electronic Imaging, pages 64920O–64920O. International Society for Optics and Photonics, 2007.
[39] L. Zhang and S. Nayar. Projection defocus analysis for scene capture
and image display. In ACM Transactions on Graphics (TOG), pages
907–915. ACM, 2006.