IEICE TRANS. INF. & SYST., VOL.E94–D, NO.5 MAY 2011

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PAPER

An Image Stabilization Technology for Digital Still Camera
Based on Blind Deconvolution
Haruo HATANAKA†a) , Member, Shimpei FUKUMOTO† , Haruhiko MURATA† , Nonmembers,
Hiroshi KANO†† , Member, and Kunihiro CHIHARA††† , Fellow

SUMMARY
In this article, we present a new image-stabilization technology for still images based on blind deconvolution and introduce it to a
consumer digital still camera. This technology consists of three features:(1) double-exposure-based PSF detection, (2) eﬃcient image deblurring
filter, and (3) edge-based ringing reduction. Without deteriorating the deblurring performance, the new technology allows us to reduce processing
time and ringing artifacts, both of which are common problems in image
deconvolution.
key words: image stabilization, blind deconvolution, deblurr, point spreadfunction, ringing reduction, digital still camera

1.

Introduction

A number of studies on image deconvolution have been
conducted in the past few years [1]. Lately, some of them
have been proposed to address hand-blur in digital still cameras [4], [5], [7], [8], [10]. It is unpractical, however, to apply them to a consumer digital camera because those image
deconvolution algorithms require precise blur detection andlong processing time and generate ringing.
We have developed a new image stabilization technology for a digital still camera based on blind deconvolution.
This new technology reduces both processing time and ringing artifacts. In this article, we will first describe our newly
developed image stabilization algorithm and the image stabilization system in which the algorithm has been implemented. Then, we will present some experimental results ofthis algorithm and system.
2.

Previous Work

There are two processing steps in blind deconvolution. The
first step is to estimate the point spread function (PSF)
caused by camera motion, equivalent to the blurring kernel.
The second step is to create the deblurred image by using
the PSF.
Manuscript received July 20, 2010.
Manuscript revised January 8, 2011.
†
The authors are with SANYO Electric Co., Ltd., Daito-shi,
574–8534 Japan.
††
The author is with Kyoto Sangyo University, Kyoto-shi, 603–
8555 Japan.
†††
The author is with Nara Institute of Science and Technology,
Ikoma-shi, 630–0101 Japan.
a) E-mail: haruo.hatanaka@sanyo.com
DOI: 10.1587/transinf.E94.D.1082

2.1

PSF Estimation

Cannon [2], Yitzhaky [3], Tanaka [4], and Moghaddam [5]proposed PSF-estimation algorithms based on the assumption that blur motions are linear and constant; however, thisassumption may not lead to satisfactory estimates and correction results because the PSFs caused by actual hand-blurare often much more complicated. Ayers [6], Fergus [7],and Shan [8] have also proposed algorithms that assess PSF
with complex shapes, but Ayers’s algorithm is unstable andFergus and Shan’s propositions are time-consuming. Besides the algorithms mentioned above, there are other estimation algorithms using images with diﬀerent exposure
times, which are devised by Lim [9] and Yuan [10]. Yet,
Lim’s PSF estimates are inaccurate, and Yuan’s method involves long iterations.
2.2

Image Deblurring

Richardson-Lucy [11], [12] and Wiener’s algorithms [13]are commonly used as blur restoration techniques. Nevertheless, their algorithms share the same problem of intensifying ringing noise when attempting to improve the deblurring eﬀectiveness. In addition, Richardson-Lucy’s iterative
approach is too time-consuming. Chalkov [14] proposed a
new algorithm to reduce the ringing noise based on the edge
strength of the deblurred image; however, the ringing noise
still remains. Although the method proposed by Yuan [10]reduces the ringing noise by restoring a residual image fromshort- and long-exposure images, the ringing eﬀect escalates
even further if image subjects move while the two images
are being taken.
3.

Proposed Algorithm

The application of image deconvolution for image stabilization in a digital still camera has three challenges. First,
the blur has to be estimated precisely without using gyro
sensors. Second, the processing time has to be shortened.
Third, the ringing artifacts in the deconvolved image need
to be suppressed without compromising the eﬀectivenessof deblurring. To solve these problems, we propose a newalgorithm called hand-blur refiner, which consists of three
distinctive functions as follows:c 2011 The Institute of Electronics, Information and Communication Engineers
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3.1 Double-Exposure-Based PSF Detector
3.1.1 Basic Algorithm
Image degradation caused by hand-blur is denoted by (1),where g and f represents the blurred image and blur-free
image, respectively. h represents the blur kernel (PSF) andn represents the noise. The convolution operator is denotedby ⊗.
g(x, y) = f (x, y) ⊗ h(x, y) + n(1)

Equation (1) becomes (2) when it comes to the frequency
domain. Here, G, F, H, and N are the Fourier transforms ofg, f , h, and n, respectively.
G(u, v) = F(u, v) · H(u, v) + N(2)There are several methods to compute the estimate ofh, which is denoted by ĥ, from g and f . The Wiener filter is
optimal in the sense that Mean Square Error (MSE) betweenĥ and h is minimal. The Wiener filter theory defines ĥ as in(3). Here, α is the regularization parameter and ∗ is the conjugate transpose matrix. InvFT denotes the inverse Fouriertransform.
G(u, v) · F ∗ (u, v)ĥ(x, y) = InvFT(3)|F(u, v)|2 + α
In a similar way, fˆ, the estimate of f , is calculated from g
and h using (4). Here, β is another regularization parameter.
∗ˆf (x, y) = InvFT G(u, v) · H (u, v)(4)|H(u, v)|2 + β
Ayers [6] proposed a method for estimating a PSF generated by a motion blur from a single blurred image (Fig. 1).
In this method, firstly, a random image is set as the initial estimate of the deblurred image f  . Next, from the deblurred
image f  and the blurred image g, a blur kernel h is calculated by (3). h is then corrected using (5) and (6). Then, adeblurred image f is created by using the blurred image g
and the corrected PSF h using (4), and the deblurred image f  is obtained by correcting f using (7) and (8). Here,

Imax is a saturated pixel value of the output image. This process is repeated until the PSF converges, and after that, theestimate is finalized.
This method, however, has two drawbacks due to its
iterative optimization procedure: (a) it requires a long processing time, and (b) it may be trapped by a local minimum,
which will result in a wrong PSF.
⎧⎪
1,
if h(x, y) > 1
⎪
⎪
⎨

h(x,
y),
if 0 ≤ h(x, y) ≤ 1
h (x, y) = ⎪
(5)
⎪
⎪
⎩ 0,
if h(x, y) < 0h (x, y)h (x, y) = h (x, y)⎧⎪if f (x, y) > Imax
I ,
⎪
⎪
⎨ max
f
(x,
y),
if 0 ≤ f (x, y) ≤ Imaxf  (x, y) = ⎪⎪⎪⎩ 0,
if f (x, y) < 0f (x, y)f  (x, y) = f  (x, y) · f  (x, y)
3.1.2

Block diagram of Ayers’ and proposed method.

(7)

(8)

Proposed Method

To address these issues, we propose a new method that uses
an image close to an ideal one without hand-blur as the initial estimate of the deblurred image instead of a random image (Fig. 1). First, two pictures are taken by pressing the
shutter: one is a regular-exposure image, and the other isa short-exposure image. The exposure time for the latteris one that reportedly prevents hand-blur nearly perfectly.
Specifically, it is set to 1/[focal length] (35-mm film equivalent). The signal gain of the short-exposure image is increased so that it can be equal to the regular-exposure image
in average luminance. Next, the short-exposure image is set
as the initial estimate of the deblurred image. Then, PSF
h is calculated from the regular-exposure image g (blurred
image) and the short-exposure image f  (initial deblurred
image) using (3). Then the same process as Ayers’ method
is repeated until the PSF converges. This method establishes
an initial estimate of a deblurred image that is approximately
equal to that of an ideal image without blur. Therefore, it results in an accurate estimate of PSF with fewer repeats andno local minimum.
However, it takes a long computation time to apply theabove process to the entire image. To solve this problem,
we select small regions with particular characteristics andestimate a unique PSF for them [15]. Specifically, we select four small patches of size 64 pixel by 64 pixel withhigh-edge strength from the short-exposure image and findeach corresponding patch from the regular-exposure image
by a block-matching method. The following m equationsin (9) can be obtained by selecting m corresponding image
patches.
gi (x, y) = fi (x, y) ⊗ h(x, y) + ni

Fig. 1(6)(i = 1, . . . , m)(9)Here, fi represents the i-th image patch in the short-exposure
image. gi represents the corresponding image patch in the

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regular-exposure image. h denotes the blur kernel, and ni
denotes noise. The m equations are transformed to the frequency domain and combined to obtain (10).
⎤ ⎡
⎤
⎤
⎡
⎡
⎢⎢⎢ G1 (u, v) ⎥⎥⎥ ⎢⎢⎢ F1 (u, v) ⎥⎥⎥⎢⎢⎢ N1 ⎥⎥⎥⎢⎢⎢⎢ G2 (u, v) ⎥⎥⎥⎥ ⎢⎢⎢⎢ F2 (u, v) ⎥⎥⎥⎥⎢⎢ N ⎥⎥⎥⎥⎥ = ⎢⎢⎢⎥⎥⎥ H(u, v) + ⎢⎢⎢⎢⎢ 2 ⎥⎥⎥⎥⎥
⎢⎢⎢
(10)
...
...
⎥⎥⎦ ⎢⎢⎣⎥⎥⎦⎢⎢⎣⎢⎢⎣ . . . ⎥⎥⎦Nm
Gm (u, v)
Fm (u, v)
Here, Gi , Fi , and Ni are the Fourier transforms ofgi , fi , and ni , respectively. When [G1 , G2 , . . . , Gm ]T ,[F1 , F2 , . . . , Fm ]T , and [N1 , N2 , . . . , Nm ]T are substituted
with G, F, and N, respectively, Eq. (10) becomes (2). Therefore, ĥ, the estimate of the blur kernel h, is obtained by (3).

window functions, to the deconvolution filter. Here, w represents the window function coeﬃcient, d denotes the distance from the center of the filter, and D is the size of thefilter. Figure 3 shows an example of applying a humming
window to a one-dimensional deconvolution filter, indicating that the ripples on both ends of the filter are suppressedby the humming window. Therefore, this method can reduce the filter size while maintaining the filter characteristic(deblurring eﬀectiveness).
w(d) =⎧⎪π (2d+D)⎪⎪,⎨ 0.54−0.46 cos
D
⎪
⎪
⎪
⎩ 0,

3.2 Eﬃcient Image Deblurring Filter

D
D
≤d≤
2
2
otherwise
(12)

if

−

If the estimated PSF does not have singular zero points, thecomplete deblurred image can be created by applying an inverse filter calculated from the blur kernel to the blurred image. However, the inverse filter obtained from the blur kernel is often singular and ill-posed. There are no clear andsimple methods to determine the size and its coeﬃcients ofa deblur kernel (deconvolution filter). To address this issue,
we propose a new algorithm to determine a small and eﬀective deblur kernel. The algorithm includes the following two
techniques:3.2.1 Trimming of Deconvolution Filter CoeﬃcientsFirstly, the deconvolution filter calculated by Wiener’s
method is trimmed by invalidating the coeﬃcients which isless than a threshold using (11).
coeﬀ(x, y) =
0,
coeﬀ(x, y),if |coeﬀ(x, y)| ≤ thresholdotherwise

Fig. 2 Relationship between PSNR and threshold for trimming filter coeﬃcients.

(11)

A larger threshold results in a smaller filter size; however,the deblurring becomes less eﬀective. Therefore, we optimized the threshold from experimental results shown in
Fig. 2. Later, a pseudo-blurred image is created by applying a known PSF to a blur-free image, and the blurred image is deblurred while changing the threshold. The peaksignal to noise ratio (PSNR) of the blur-free image and the
deblurred image is then calculated to evaluate the result. A
larger PSNR infers a better result as shown in Fig. 2. The
optimal threshold is determined to be the point where PSNR
begins to decrease rapidly. This technique achieves smaller
deconvolution filter while maintaining PSNR.
3.2.2 Window Function
The second technique is to condense the deconvolution filter. Generally, applying a window function to a filter suppresses the ripples in the filter without altering the frequency
response. Our method proposed in this article applies ahamming window (12), which is one of the representative

Fig. 3

Example of window function.

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Fig. 4Block diagram of edge-based ringing reduction.

Fig. 5
Table 1

Experiment of PSF estimation.

Comparison between conventional and proposed methods.

3.3 Edge-Based Ringing Reduction
As stated in “2. Previous Work,” some approaches forinhibiting ringing noise have been proposed, but they allpresent an issue of reducing the deblurring eﬀectiveness. To
solve this problem, we propose a new method to reduce ringing using humans’ visual characteristic without lowering the
deblurring eﬀect. The basic idea is that ringing is perceived
from the low-frequency image areas, while deterioration due
to hand-blur is perceived from the high-frequency image areas, so if we blend a blurred image and a deconvolved image
using pixel-wise weights based on spatial frequency, we caneﬀectively removes the ringing. Figure 4 shows the block
diagram of our proposed method.
First, a deblurred image is generated from a blurred
image using methods described earlier. Next, we generate an edge-extracted image by applying 3 × 3 Prewitt filter to the blurred image. Then, weighted-addition of theblurred and deblurred images is carried out on the basis of
the weight of the pixel value of the edge-extracted image using (13), where r, b, d, and e, represents the restored image,blurred image, deblurred image, and edge-extracted image
respectively. w represents the weight for addition. emax andemin denotes the maximum and minimum value of e. Thismethod eﬀectively removes ringing in the low-contrast area
where ringing is noticeable as well as prevents reduction of
sharpness, which is an indicator of the deblurring eﬀect.
r(x, y) = (1 − w(x, y)) · b(x, y) + w(x, y) · d(x, y)e(x, y) − eminw(x, y) =emax − emin
4.

time of 1/15 s and a focal length of 105 mm (35-mm equivalent) (Fig. 5). A panel-printed dotted pattern was placed in
front of a resolution chart and 100 pictures of the chart weretaken. Both Ayers’ method and our double-exposure-based
PSF detector were applied to the pictures. A comparison
between the estimated PSFs and the real blur of the dotted
pattern was conducted by evaluating the diﬀerence in shapeand size. We defined the diﬀerence as D shown in (14),where h1 and h2 represents the estimated PSF and the real
blur respectively. Dil and Bin denotes the 3 × 3 dilationand thresholding operation respectively. The PSF estimation was judged as a success if the diﬀerence D is 0. Table 1
shows their success ratios and processing times. Besides reducing the processing time by 100 times, we have improved
the success ratio by 40 %.
⎧1, if Dil(h1 (x, y)) < h2 (x, y)⎪⎪⎪⎨or Dil(h2 (x, y)) < h1 (x, y)
D=
⎪
⎪
⎪
⎩
D=0
(14)
0, otherwise,h (x, y)1 = Bin(h1 (x, y)), h2 (x, y) = Bin(h2 (x, y))
4.2

(13)

Experimental Results

We have evaluated the performance of the three methods ofthe hand-blur refiner using PC simulation.
4.1 Blur Detection Performance
Tests were conducted for images taken with an exposure

Deconvolution Performance

Blurred images were created by applying 20 diﬀerent blurkernels to a blur-free image of the test chart and were deconvolved with both conventional and proposed deconvolution
filters. Here, 512 pixel by 512 pixel filter size is employed
for the conventional Wiener filter so as to maximize the deblurring performance. Proposed method 1 only applies thetrimming technique while proposed method 2 applies both
trimming and window function techniques. The processing
time was measured via PC simulation and the PSNR valuesof each restored image and blur-free image pair were calculated respectively. Larger PSNR indicates better deblurring

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result. The results are shown in Table 2. Compared to theconventional method, the proposed methods 1 and 2 on average requires shorter processing times by roughly a factor of
60 and 100, respectively, while maintaining similar PSNR
values.
4.3 Ringing Reduction Performanceringing reduction method. The blurred images were createdby applying 20 diﬀerent blur kernels to a blur-free imageof the test chart and were deconvolved with / without the
proposed edge-based ringing reduction.
Figure 7 is an example of these images. We calculated
each PSNR of the restored and the blur-free images. The
results are shown in Table 3. Using our method, the average
PSNR increased by 5 dB.

Figure 6 shows the experimental procedure to evaluate ourTable 2Comparison between conventional and proposed methods.

5.

Prototype Camera

5.1

System Configuration

We have implemented the hand-blur refiner into a prototype
digital still camera shown in Fig. 8.
Table 4 and Fig. 9 show the specifications and the block
diagram of the camera system, respectively. Once the shutter button is pressed, both regular- and short-exposure images are taken and then loaded into the frame memory. After
that, the Digital Signal Processing (DSP) estimates the PSF
using both images, calculates the deconvolution filter coeﬃcients, and sets up the Finite Impulse Response (FIR) filter.
The FIR filter performs both filtering and deringing on theregular image and outputs the result to the LCD monitor as

Fig. 6

Experimental procedure for our ringing reduction.

Fig. 8 Prototype camera in which hand-blur refiner is implemented
impimplemented.
Table 4

Specifications of the prototype camera.

Fig. 7 Example images. (a) Blur-free image. (b) Blurred image. (c)Restored image without ringing reduction. (d) Restored image with ringing
reduction.
Table 3 PSNR comparison between the two methods with and without
ringing reduction.

Fig. 9Block diagram of hand-blur refiner.

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well as the memory card.
5.2 Overall Performance
The total performance of image stabilization is evaluated using a prototype camera in which the hand-blur refiner is implemented. PSNR is not suitable for evaluating the performance of image stabilization in practice because the PSNR
is greatly influenced by the accuracy of the image registration of the blur-free image and the restored image taken at
diﬀerent time. We proposed an original method for measuring the actual performance of image stabilization. In our
method, the area of the region surrounded by the Modulation Transfer Function (MTF) curve, which indicates the
sharpness of images, serves as an indicator of the degree ofhand-blur. This is because at the time of shooting the larger
the hand blur is, the smaller the area of MTF in the image
will be (Fig. 10). As a subject, we use a circular zone plate(CZP) that enables us to measure the MTF in all directions.
Evaluation results of the system using this method areshown in Fig. 11. The filled diamond points indicate the
MTF area values for real blurred images taken by the camera held in hand. The square points indicate the values forrestored images. Each point indicates the averaged MTF
area of 100 images of CZP taken with diﬀerent exposure
times. The curves are approximated by a quadratic function
to fit these points. From these curves, exposure time T B of
the blurred image and exposure time T R of the restored image are extracted, both of which correspond to the same areaof MTF. Then, a gain of exposure time N [EV] is obtainedusing (15). This indicator tells how the deblurring eﬀect canmake the exposure time longer for shooting without handblur. A larger N will obtain a higher deblurring eﬀect.

N = log2 (T R /T B )(15)

Table 5 shows the results of calculating all points on
the curves using the equation above. The gain of exposure
time by our method was in the range from 0.7 EV to 1.4 EV.
The process time for an 8-M image was 3.1 s on average andFig. 10

MTF values for blurred and deblurred images.

Fig. 11

Deblurring results for diﬀerent exposures time.

Table 5

Total performance of our system.

Fig. 12 Comparison of restored images. (a) Original image with hand-blur. (b) Restored image withhand-blur refiner. (c) Restored image with conventional method.

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Fig. 13 Comparison of restored images. (a) Original image with hand-blur. (b) Restored image withhand-blur refiner. (c) Restored image with conventional method.

4.9 s at maximum.
Finally, we present a comparison between the restored
images with the hand-blur refiner and a conventional blind
deconvolution method, as shown in Fig. 12 and Fig. 13. The
conventional method is based on an iterative procedure,
known as the deconvblind function in MATLAB.
6.

Conclusion

We have developed a new image stabilization technology
based on blind deconvolution and successfully implemented
it in a digital still camera. Evaluation results show that in the
1/125–1/8 s exposure time range we have achieved a 0.7–
1.4 EV exposure time gain as well as reduced the processing
time to 3 s on average.
Although we have proved that our technology can successfully perform in an actual digital camera, there is stillroom for improvement. One possibility is to remove thespatially-variant blur. To do that, a scheme that estimatesblur kernels for small regions would be required. Anotherpotential improvement is to reduce the shooting time for thesecond image. To achieve that, it would be eﬀective to use alive preview image, right before pressing the shutter, insteadof shooting the short-exposure image.
Moreover, the application of this algorithm in conjunction with optical hand-blur correction system can yield evenbetter deblurring performance. Furthermore, our technology
is applicable not only to image degradation by hand-blur but
to image degradation by defocusing as well.
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Haruo Hatanaka
received his B.E. inmechanical system engineering from Hiroshima
University, Japan in 1991. In the same year, hejoined the R&D Headquarters of SANYO Electric Co., Ltd., to engage in research into the image processing technologies for digital cameras
and video cameras. He is a manager at Audio and Image Processing Department in Digital
Technology Research Center.

Shimpei Fukumotoreceived his B.E.
in computer science and systems engineeringfrom Kobe University, Japan in 1998., Then, hejoined the R&D Headquarters of SANYO Electric Co., Ltd., where he has been researching on
image processing technologies. He is a chief researcher at the Audio and Image Processing Department in Digital Technology Research Center.

Haruhiko Murata
received his B.E. instrumentation engineering from Kobe University in1984 and his Ph.D. in image processing engineering from the Nara Institute of Science andTechnology in 2001. Since 1984, he has been
engaged in research on image processing technologies, and he is currently a general manager
at the Digital Technology Research Center in
SANYO Electric Co., Ltd.

Hiroshi Kano
received his BE, ME, and DE
degrees in information engineering from Kyoto
University in 1982, 1984, and 1998 respectively.
From 1998 to 2010, he had been engaged in research on image processing and computer visionin SANYO Electric Co., Ltd.,, and he is currently a professor at the Kyoto Sangyo University, Japan.

Kunihiro Chihara
received his BE, ME,
and DE degrees in engineering science from
Osaka University in 1968, 1970, and 1973 respectively. He is currently a professor at theGraduate School of Information Science, Nara
Institute of Science and Technology (NAIST),
Japan. His research interests include ubiquitous computing, virtual reality, and multimedia.
He is a member of IEEE, ACM, JSUM, ISCIE,VRSJ, and JSMBE.