Full Spherical High Dynamic Range Imaging from the Sky
Fumio Okura, Masayuki Kanbara, Naokazu Yokoya
Graduate School of Information Science
Nara Institute of Science and Technology (NAIST)
Nara, Japan
Email: {fumio-o,kanbara,yokoya}@is.naist.jp

Abstract—This paper describes a method for acquiring fullspherical high dynamic range (HDR) images with no missing
areas by using two omnidirectional cameras mounted on the
top and bottom of an unmanned airship. The full spherical
HDR images are generated by combining multiple omnidirectional images that are captured with different shutter speeds.
The images generated are intended for uses in telepresence,
augmented telepresence, and image-based lighting.
Keywords-omnidirectional camera; high dynamic range;
telepresence; augmented telepresence; image based lighting(a) Image captured from top of airship

I. I NTRODUCTION
This paper proposes an aerial imaging system that cancapture full spherical images without missing areas and withhigh dynamic range (HDR) for telepresence [1], imagebased lighting (IBL) [2], and augmented telepresence [3].
It should be noted that a full spherical image means animage covering the 360◦ × 360◦ field of view of a fullsphere. Telepresence is a technology providing a user witha view of a remote site with high immersiveness and is oneof the major research fields in virtual reality. In particular,
offline telepresence uses preliminarily recorded spherical(omnidirectional) images to provide users with views in any
direction from a location and has become very importantwith the popularization of walk-through applications such as
Google Street View [4]. Recently, there have been studies
dealing with augmented telepresence [3], which provides
the user with both the view of a remote site and related
information by using augmented reality (AR) techniques [5].
The augmented telepresence system [3] not only realizes
offline telepresence using recorded aerial spherical images
but also renders CG models of buildings with IBL, which isa high-quality lighting technique using spherical images asenvironmental light maps [2].
This paper proposes an aerial imaging system to resolve
two problems when spherical images are observed by omnidirectional cameras for telepresence, IBL, and augmented
telepresence: 1) occurrence of missing areas, and 2)
deficiency of dynamic range. In the rest of this paper,
each problem and the related works are specified, and theapproaches that we employed are described.

(b) Image captured from bottom of airship(c) Full spherical image
Figure 1. Full spherical HDR image generated from multi-exposure images
captured by two omnidirectional cameras visualized with same exposure.

II. P ROBLEMS AND RELATED WORKS
A. Occurrence of missing areas
Missing areas sometimes appear in a spherical image asshown in Figs. 1(a) and 1(b). These areas are caused by therestriction of the field of view of the omnidirectional cameraand (in the case of Fig. 1) by occlusions from the airship.
Such areas decrease the immersiveness in telepresence andmake it impossible to use the spherical images for IBL.
The approaches to fill the missing areas are divided into
two categories as follows:
1) Approaches not using observed intensities. A previous
work on augmented telepresence [3] filled a missing
area in the upper hemisphere of spherical videos byusing a sky model that represents a statistical model ofvarious weather conditions. Another work using image
inpainting techniques [6] filled the missing areas fromthe image itself. These are categorized as approaches
not using observed intensities of missing areas such aswith a model-based approach.
2) Approaches using observed intensities. Kawai etal. [7] treated a missing area in the ground portionof spherical video sequences captured in motion. The
intensities of the missing area were estimated fromthe intensities of the images that observed the sameplace from other viewpoints. This is categorized as one
approach using observed intensities of missing areas.
The former category of approach does not aim to estimate
the real intensities in the missing areas but aims to fill themissing areas to decrease the strange appearance. The intensities estimated by the approach may have great differencesfrom the real intensities. This is a cause of great differencesin shade or shadow between real and virtual objects in IBL.
Hence, this study applies the latter category of approach, bywhich the real intensities can be observed. In this study, twoomnidirectional cameras mounted on the top and bottom ofan unmanned airship capture the whole scene as shown in
Fig. 1(c).

1) Multi-exposure imaging using
two omnidirectional cameras
mounted on an unmanned airship
2) HDR image generationfrom multi-exposure images3) Combination of HDR images
captured by two cameras
Figure 2. Flow of proposed aerial imaging. Right images are examplesof output of each process.

HDR histogram generated from previously captured images.
An omnidirectional camera mounted atop the vehicle mainly
captures the sun and sky, which are too bright to capture
without saturation even if the highest shutter speed is used.
Neutral density (ND) filters are attached to the camera to
overcome the problem as was done in related work [9] that
directly captured the sun and sky.
In this paper, the process of the proposed system is divided into three parts: 1) multi-exposure imaging using two
omnidirectional cameras mounted on an unmanned airship,
2) HDR image generation from multi-exposure images, and3) combination of HDR images captured by two cameras.
This paper also shows experimental results of generating fullspherical HDR images using aerial image sequences.
III. F ULL SPHERICAL HDR IMAGING

B. Deficiency of dynamic range

A. Overview

Spherical images without saturation of intensity are recommended for IBL [8]. However, the dynamic range ofoutdoor scenes is too high to capture using standard camerasthat have normal (8-bit) dynamic range, as the sun canbe 217 times brighter than the dark areas in the clouds or
the sky [9]. An HDR imaging method using low dynamic
range (LDR) images captured with multiple exposure values (multi-exposure images), as proposed by Debevec andMalik [8], is widely used to capture a scene by means of
LDR cameras without saturation. We employ this method togenerate spherical HDR images.
When the images are captured in motion, misalignment
appears among the multi-exposure images. Although there
have been some studies to overcome this problem [10], it remains difficult to correct a large misalignment. It is better toapply fewer exposure values in the case that camera velocity
and frame rate are constant. In this study, to generate HDR
images effectively from a small number of multi-exposure
images, shutter speeds are controlled automatically using an

The proposed system generates full spherical images using
multi-exposure images captured with two omnidirectional
cameras mounted on the top and bottom of an unmanned
airship. The process of the proposed system is divided into
three parts as shown in Fig. 2. Each part is described indetail in the following paragraphs.
1) Multi-exposure imaging using two omnidirectional
cameras mounted on an unmanned airship. Multiexposure images are captured with multiple shutter
speeds using two omnidirectional cameras mounted on
the top and bottom faces of an unmanned airship. Since
the camera atop the vehicle mainly captures the sunand sky, ND filters are attached to that camera. In
addition, shutter speeds are controlled automatically so
as to capture multi-exposure images effectively.
2) HDR image generation from multi-exposure images.
HDR spherical images from two omnidirectional cameras are respectively generated using multi-exposure images. There are misalignments among the multi-exposureFigure 3. Unmanned airship and omnidirectional cameras. Two cameras
are mounted on the top and bottom of the airship. ND filters are attachedto the camera on the top. A GPS antenna is also mounted atop the airship.

images caused by the changing position and orientationof the camera. We deal with misalignments due to change
in the orientation of the camera, which dominantly
affects the amount of misalignment. These are correctedby estimating the change in camera orientation from thecaptured image sequence.
3) Combination of HDR images captured by two cameras. Full spherical HDR images are generated from
HDR images from two different cameras. In this study,
it is difficult to fix the relative geometric relationshipbetween the cameras, because we use an unmanned
airship that has a deformable body. The two HDR images
are aligned by estimating the relative rotation between
the cameras in every frame. In addition, we correct achromatic change that occurs in the images captured with
ND filters.
B. Multi-exposure imaging using two omnidirectional cameras mounted on an unmanned airship
1) Configuration of the aerial imaging system: A radio
controlled unmanned airship that is 12 m long (Fig. 3) isused as the platform for aerial imaging. Spherical image
sequences with missing areas are captured using two omnidirectional multi-camera systems (Point Grey Research
Ladybug2, see Table I) mounted on the top and bottom of theairship. The cameras are connected to a laptop PC for storing
the captured images with time-stamps. ND filters (Fujifilm
Corp., ND 2.0) that transmit 1% of the light are attached tothe camera on the top of the vehicle as was done in a related
study that directly captured the sun and sky [9]. Figs. 1(a)and 1(b) show panoramic images generated by removal oflimb darkening and geometric transformation [11]. Notethat the amount of limb darkening is different between theimages captured with and without ND filters. The airship is
also equipped with a differential GPS (Hitachi Zosen Corp.,
P4-GPS) to acquire the capture positions of the images,
which can be used for superimposing virtual objects in
augmented telepresence.

2) Automatic control of shutter speeds: The shutter
speeds used to capture multi-exposure images are automatically controlled using previous images. Many still cameras
provide an auto-bracketing function for capturing multiexposure images. Those cameras determine one correct exposure from the brightness of the scene, and fixed multiplesof the original exposure value are used as the other exposure
values [12]. Grossberg and Nayar [13] proposed a method
to determine unfixed exposure sets from the dynamic rangeof the scene. To determine a more appropriate exposure
set in our study, an HDR histogram of the previous multiexposure image sequence is used to reduce the effects oflarge quantization steps on human vision, such as pseudoedges due to the small number of multi-exposure images.
New shutter speeds snew1 , snew2 , ..., snewn are determined by multi-exposure images captured using old shutter
speeds sold1 , sold2 , ..., soldn . We use n = 4 as the numberof shutter speeds because the omnidirectional Ladybug2
cameras used in our experiment have four registers to store
the shutter speeds.
The highest shutter speed snew1 is simply determined suchthat the scene can be captured without saturation from theimage captured using sold1 :
0.5sold1(Lmax1 = 255)

255 + θsh(1)
snew1 =
sold1 (Lmax1 ≤ θsh )

2L

max
1

sold1
(otherwise),
where Lmax1 (sold1 ) denotes the maximum intensity in theimage captured using sold1 . Further θsh is a threshold that
limits snew1 to θsh < Lmax1 < 255 in case the response
curve is linear, and θsh = 192 is used experimentally in
Section IV.
When a small number of multi-exposure images are
acquired, the effects of large quantization steps, such as
pseudo-edges, can appear in the HDR image generated.
The other shutter speeds snew2 , ..., snewn are determined
to reduce such effects. To estimate the shutter speeds thathave minimum quantization steps, an HDR histogram is
calculated from the captured images as illustrated in Fig. 4.
Hmaxi (snewi ) is the maximum intensity to capture using
the ith highest (i ≥ 2) shutter speed snewi in the HDR
Table IS PECIFICATIONS OF L ADYBUG 2
Item
Number ofcamerasResolution
FOV
CCD
Frame rate
Number of
shutter registers

Specification
6(five in horizontal ring, and one on top)
1024×768 (for each CCD)
More than 75% of the full sphereSony ICX204AK
Maximum 30fps
4(to capture multi-exposure images)

Transition of shutter speeds

sle
ixp
fo
re
b
m
uN

(a) Estimating rotation between images that have same shutter speeds0‫܀‬,

Intensity

Figure 4. Example of HDR histogram for n = 4, where ∆i denotes
quantization steps and Hmaxi is the maximum intensity that can be
captured using shutter speed snewi . Further, M3 is the number of pixels
in the filled area of the histogram, and the other Mi are determined in asimilar fashion.

‫܀‬,‫܀‬,‫܀‬,

(b) Calculating rotation between neighboring images using sphericallinear interpolation

Reference image(c) Calculating rotation to reference image from other images
Figure 6.

Process of multi-exposure image alignment.

∆i and the number of pixels corresponding to the shutter
speed snewi :∑Es =(Mi ∆i ).
(5)i(a) Without alignment(b) With alignment

Figure 5. HDR images with and without multi-exposure image alignment.

histogram. Mi (snewi ) is denoted using Hmaxi (snewi ) as
Hmaxi

Mi =

∑

jk ,

(2)

k=H

where jk is the number of pixels whose intensity is k, andH is defined as{0(i = n)H=(3)
Hmaxi+1 (otherwise).
When the response curve of the camera is regarded as linear,
the quantization step corresponding to snewi on the HDR
histogram can be calculated as follows:
Hmaxi
.
(4)
255
Finally, snewi (i ≥ 2) is determined by minimizing theenergy function Es with respect to changing snewi (i ≥ 2),
where Es is the sum of the products of the quantization step∆i =Es is a linear function that has multiple local minimums.
We apply a simple coarse-to-fine search technique to find areasonable solution.
The determination processes of the shutter speeds arerepeated every several seconds to allow changes of lighting
environment in the real world. Note that snew1 may notconverge within a single cycle, and thus several cycles may
be needed. Eq. 4 can be changed to apply another nonlinear
response curve.
C. HDR image generation from multi-exposure images
1) Alignment of multi-exposure images: There are misalignments among multi-exposure images due to the changing position and orientation of the camera while capturing
image sequences. This causes blurring in HDR images
generated from misaligned multi-exposure images, as shownin Fig. 5(a).
We assume that the amount of misalignment is dominantly
affected by change in the orientation of the camera, sinceaerial views are mainly taken from a long distance. Accordingly, we correct the misalignments, as shown in Fig. 5(b),by estimating the change in the camera orientation from thecaptured image sequence.
Fig. 6 shows the steps to align the multi-exposure images. In the first step, two images having the same shutter
speeds are selected, and the camera rotation between theselected images is estimated as shown in Fig. 6(a). Among
multi-exposure images, those having the fewest saturated
pixels (intensity of 255) and underexposed pixels (intensityof less than 16 in our implementation) are selected. The
corresponding points (pm , qm ) between the selected images
are determined using the KLT tracker [14] and are projected
onto the unit sphere. The parameters for rotation Rci are
estimated by nonlinearly minimizing the energy function El
defined as the sum of squares of the Euclidean distances ofthe projected corresponding points |pm , qm |:∑El =|pm , qm |2 .
(6)m

RANSAC is used to reduce errors due to mismatches.
Then, as shown in Fig. 6(b), parameters for rotation
between each two adjacent images Rf(i,j) are calculated byinterpolating Rci using spherical linear interpolation.
Parameters for rotations to an arbitrary reference image from neighboring images are calculated from Rf(i,j)
as shown in Fig. 6(c). Multi-exposure images aligned to
the reference image are acquired by transformation usingparameters for rotation to the reference image. To generatealigned multi-exposure images for a whole video sequence,
Rf(i,j) must first be calculated in a whole sequence, and theneach frame in the sequence is used as the reference image.
2) HDR image generation: An HDR imaging method for
multi-exposure images proposed by Debevec and Malik [8],which is widely used to capture HDR scenes using LDR
cameras, is used to compose intensity in multi-exposure
LDR images. In this study, it is necessary to consider lightattenuation due to ND filters attached to the camera on the
top. If the response curve is linear or linearized in advance,
intensities in HDR image Ih are calculated using the LDR
intensity Il , the nominal light transmittance η of the ND
filter, and the shutter speed t[s] of the LDR image:{ I
γ tl (without ND ﬁlters),
Ih =
(7)
Il
γ ηt
(with ND ﬁlters).

Figure 7. Mask images used for alignment of images from two cameras.
Top left: Black pixels indicate a missing area from the top of the airship.
Bottom left: Black pixels indicate a missing area from the bottom of theairship. Right: White pixels indicate an overlap area.

(a) Without ND filter (image captured from bottom of airship).
Average intensity: (R, G, B) =(111.5, 108.3, 119.7)(b) With ND filter (image
captured
from
top
of
airship).
Averageintensity:(R, G, B) = (95.1, 103.5, 111.9)

Figure 8. Example of chromatic change due to ND filter. A pair of HDR
images converted to the same exposure are shown for a close-up of a partof the overlap areas.

the omnidirectional image does not change significantly. The
area displayed as white pixels in the right of Fig. 7 shows
the overlap area where the scene can be captured from both
cameras without occlusions. This region is defined as the
conjunction of negation of the missing areas in each image.

Although the scale factor γ is meaningless when only the
relative pixel intensities are required, it can be determined
appropriately to calculate physical absolute radiance values[W/sr/m2 ]. The HDR intensities calculated from each setof multi-exposure images are composed in accordance withthe method of Debevec and Malik [8].

The corresponding points are detected by using the KLT
tracker [14] on the feature points in the overlap area in
the image captured from the bottom of the airship. The
parameters for rotation between the cameras from top tobottom are determined using detected correspondences inthe same way as in Section III-C1.

D. Combination of HDR images captured by two cameras2) Correction of chromatic change due to ND filters :Although ND filters are designed to transmit all wavelengthsof light equally, the ND filter actually causes chromaticchange [9]. The ND filters used in our experiment transmit
less red light, as shown in Fig. 8. The chromatic changes
are corrected by estimating linear transformation parametersfrom intensity in the overlap areas of the images from two
cameras. The RGB values (Rtop (x), Gtop (x), Btop (x)) fora pixel x in the image captured from the top of the airship

1) Alignment of HDR images captured by two cameras:
The two HDR images captured from the top and bottom of
the airship are aligned by estimating the relative rotation between the cameras. To estimate the parameters for rotation,
the missing areas in the images captured by the two cameras
are manually masked in advance, as shown in the left ofFig. 7. Note that only one mask image is required for eachcamera over the whole sequence, because the missing area in(a) Top: 0.1ms (with ND filters)(b) Top: 4.1ms (with ND filters)(c) Top: 14.4ms (with ND filters)(d) Top: 43.3ms (with ND filters)(e) Bottom: 0.1ms(f) Bottom: 0.4ms(g) Bottom: 1.1ms(h) Bottom: 2.4ms

Figure 9.

Captured multi-exposure images and corresponding shutter speeds.

(a) Exposure as for Fig. 9(e)(b) Exposure as for Fig. 9(h)Figure 10. Full spherical HDR images with cropped intensity and with exposures equal to thoseof multi-exposure images.

are converted by the following linear transformations:
′
Rtop
(x) =
′
Gtop (x) =
′
(x) =
Btop

βr Rtop (x),βg Gtop (x),βb Btop (x),

(8)
(9)
(10)

where βr is estimated byβr =Rbot (x)Σx∈A R
top (x)

NA

.

(11)

Note that (Rbot (x), Gbot (x), Bbot (x)) denote the RGB values of the pixel x in the image from the bottom of theairship. A denotes the overlap area, and NA indicates thenumber of pixels belonging to A. In the same way, βg andβb are also estimated.
3) Combination of corrected HDR images from two cameras: Full spherical images are finally generated by combining the pairs of HDR images with alignment and chromatic
correction. The intensities in the overlap area in the fullspherical image are determined by alpha blending of two
images. From the intensities Itop and Ibot of a pixel inthe overlap areas of two cameras, the intensity If ull of thecorresponding pixel of a full spherical image is calculatedas
If ull = αIbot + (1 − α)Itop ,(12)

Figure 11. Full spherical image tone-mappedin accordance with the proposal of Reinhard etal. [15]

where α varies linearly between zero on the upper boundaryof the overlap area and unity on the lower boundary.
IV. E XPERIMENTAL RESULTS
A. Experiment using still images
To confirm that the HDR image generated by the proposed method represents the real environment reasonably,
we conducted an experiment to generate a full spherical
HDR image using still images captured using an unmanned
airship above our campus. The airship was at 130 m inaltitude and moved at 3 m/s while capturing multi-exposure
images. The captured multi-exposure images and corresponding shutter speeds are shown in Fig. 9. Figs. 1(a)and 1(b) show the HDR images from the top and bottomof the airship, respectively. The full spherical HDR image
composed using Figs. 1(a) and 1(b) is shown in Fig. 1(c).
It is shown that the two images from the top and bottom
were combined successfully by the alignment and chromatic
correction without any artifacts. Fig. 10 shows the fullspherical images visualized with exposures equal to thosein Figs. 9(e) and 9(h), respectively. We confirm that thefull spherical HDR image reproduces the captured multiexposure images reasonably. The full spherical HDR image
can also be visualized by typical tone-mapping methods
such as that proposed by Reinhard et al. [15], as shown in
Fig. 11. Tone-mapped full spherical images are suitable forFigure 12.

(a) 1st frame(b) 100th frame(c) 200th frame(d) 300th frame(e) 400th frame(f) 500th frame

Full spherical tone-mapped images generated from video frames. The images were aligned using the estimated camera orientation [16].

immersive telepresence, providing the user with the senseof being able to look around the location, with the specifictextures of various radiances.
B. Experiment using video sequence
To generate full spherical HDR videos that can be applied
to augmented telepresence, we employed the approach to avideo sequence consisting of 500 frames. The airship was
at 130 m in altitude and moved at 5–8 m/s while capturingmulti-exposure images. The frame rate of the generated
HDR video was 16 fps, which is same as that of the source
multi-exposure video. The processing time was about 10 s
per frame. The position and orientation of the camera on
the bottom of the airship were estimated by the structurefrom-motion approach for omnidirectional cameras [16], andFig. 12 shows frames of the full spherical images aligned
using camera orientation. The position and orientation of thevideo frames were successfully estimated. This information
can be used for geometric registration between real andvirtual objects in augmented telepresence.
C. Discussions about acquirable dynamic range
In the result, several pixels around the sun are stillsaturated in spite of exploiting the image captured with ND
filters using the highest shutter speed s = 0.1 ms, whichis the smallest value selectable for the camera. In the caseof Fig. 9, the dynamic ranges that can be captured without
saturation and underexposure are 1 : 28.58 for the cameraon the bottom of the airship, and 22.47 : 215.23 for thecamera on the top, when saturation intensity is defined as
255 and underexposure intensity is defined as less than 16.
This indicates that the dynamic range of the camera atop
the airship is too low to capture the dynamic range of thesky, which can approach 1 : 217 . In the experiment it isnecessary to use ND filters with less light transmission tocapture images without saturation around the sun. To selectND filters, we also have to consider the negative effects on
the appearance and the alignment processes due to a growthin underexposed pixels.
V. C ONCLUSIONSThis paper focused on an aerial HDR imaging systemgenerating full spherical images without any missing areas.
The full spherical HDR images generated can be applied
to telepresence, IBL, augmented telepresence, and otherapplications using omnidirectional images. Multi-exposure
images used to generate HDR images are captured by a pairof omnidirectional cameras mounted on the top and bottomof an airship, which is the platform for aerial imaging. In
experiments, full spherical HDR images were successfully
generated from image sequences captured from the sky
above our campus. It has also been confirmed that a fullspherical HDR video can be generated with the positionand orientation information of the camera for applying
to augmented telepresence. In future work, we will use
full spherical HDR images for augmented telepresence by
IBL approaches using information on camera position andorientation.
ACKNOWLEDGMENT
This research was partially supported by Grant-in-Aid for
Scientific Research (A), No. 23240024, from the Japanese
Ministry of Education, Culture, Sports, Science and Technology (MEXT) and by the “Ambient Intelligence” project
funded by MEXT.

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