Source: http://www.patentsencyclopedia.com/app/20130016244
Timestamp: 2017-02-19 21:22:05
Document Index: 676056948

Matched Legal Cases: ['art 20', 'art 41', 'art 55', 'art 57', 'art 60', 'art 61', 'art 20', 'art 61', 'art 20', 'art 41', 'art 41', 'art 53', 'art 53', 'art 53', 'art 53', 'art 53', 'art 41', 'art 55', 'art 41', 'art 55', 'art 55', 'art 83', 'art 57', 'art 55', 'art 55', 'art 60', 'art 60', 'art 57', 'art 61', 'art 60', 'art 61', 'art 81', 'art\n82', 'art 83', 'art 81', 'art 82', 'art 83', 'art 82', 'art 81', 'art 83', 'art 55', 'art 55', 'art 83', 'art 55', 'art 55', 'art 55', 'art 82', 'art 82', 'art 101', 'art 102', 'art 101', 'art 101', 'art 101', 'art 102', 'art 102', 'art 101', 'art 102', 'art 103', 'art\n103', 'art 83', 'art 83', 'art 103', 'art 55', 'art 55', 'art 201', 'art 202', 'art 203', 'art 204', 'art 205', 'art 201', 'art 55', 'art 202', 'art 202', 'art 202', 'art 205', 'art 83', 'art 202', 'art 203', 'art 202', 'art 204', 'art 203', 'art 203', 'art 101', 'art 101', 'art 102', 'art 102', 'art 103', 'art 103', 'art 83', 'art 55', 'art 205', 'art 83', 'art 205', 'art 202', 'art 202', 'art 202', 'art 203', 'art 204', 'art 204']

IMAGE PROCESSING APARATUS AND METHOD, LEARNING APPARATUS AND METHOD, PROGRAM AND RECORDING MEDIUMAANM TAKAHASHI; NoriakiAACI TokyoAACO JPAAGP TAKAHASHI; Noriaki Tokyo JPAANM NAGANO; TakahiroAACI KanagawaAACO JPAAGP NAGANO; Takahiro Kanagawa JP - Patent application
Patent application title: IMAGE PROCESSING APARATUS AND METHOD, LEARNING APPARATUS AND METHOD, PROGRAM AND RECORDING MEDIUMAANM TAKAHASHI; NoriakiAACI TokyoAACO JPAAGP TAKAHASHI; Noriaki Tokyo JPAANM NAGANO; TakahiroAACI KanagawaAACO JPAAGP NAGANO; Takahiro Kanagawa JP
Noriaki Takahashi (Tokyo, JP)
Takahiro Nagano (Kanagawa, JP)
Patent application number: 20130016244
There is provided an image processing apparatus including a calculation
part calculating a prediction value of a target pixel in an image
captured by an image capturing part capturing images using an image
sensor configured by regularly arranging a plurality of pixels having a
plurality of exposure times based on values of a plurality of other
pixels different from the target pixel in exposure time and prediction
coefficients corresponding to the respective other pixels; and a motion
amount identifying part identifying a motion amount of the target pixel
per unit time based on the calculated prediction value of the target
pixel and a value of the target pixel.Claims:
1. An image processing apparatus comprising: a calculation part
calculating a prediction value of a target pixel in an image captured by
an image capturing part capturing images using an image sensor configured
by regularly arranging a plurality of pixels having a plurality of
exposure times based on values of a plurality of other pixels different
from the target pixel in exposure time and prediction coefficients
corresponding to the respective other pixels; and a motion amount
identifying part identifying a motion amount of the target pixel per unit
time based on the calculated prediction value of the target pixel and a
value of the target pixel.
2. The image processing apparatus according to claim 1, further
comprising: a coefficient supply part supplying the prediction
coefficients to the calculation part, wherein the coefficient supply part
supplies the prediction coefficients corresponding to a preset pattern of
motion to the calculation part, and the calculation part calculates the
prediction value of the target pixel for each pattern of motion using a
prediction expression based on the values of the plurality of other
pixels and the prediction coefficients corresponding to the respective
other pixels.
3. The image processing apparatus according to claim 1, wherein the
motion amount identifying part identifies the motion amount of the target
pixel per unit time based on a prediction error between the prediction
value of the target pixel calculated for each preset pattern of motion
and the value of the target pixel.
4. The image processing apparatus according to claim 1, wherein the
prediction coefficients are prediction coefficients previously learned by
a learning apparatus, and the learning apparatus includes: a blur image
generation part generating a blur image obtained by adding motion blur
corresponding to a plurality of preset patterns of motion to the image
captured by the image sensor; and a coefficient calculation part
calculating, corresponding to the respective plurality of patterns of
motion, the prediction coefficients for calculating the prediction value
of the target pixel in the captured image based on the values of the
plurality of other pixels different from the target pixel in exposure
5. An image processing method comprising: calculating, with a calculation
part, a prediction value of a target pixel in an image captured by an
image capturing part capturing images using an image sensor configured by
regularly arranging a plurality of pixels having a plurality of exposure
times based on values of a plurality of other pixels different from the
target pixel in exposure time and prediction coefficients corresponding
to the respective other pixels; and identifying, with a motion amount
identifying part, a motion amount of the target pixel per unit time based
on the calculated prediction value of the target pixel and a value of the
6. A program causing a computer to function as an image processing
apparatus comprising: a calculation part calculating a prediction value
of a target pixel in an image captured by an image capturing part
capturing images using an image sensor configured by regularly arranging
a plurality of pixels having a plurality of exposure times based on
values of a plurality of other pixels different from the target pixel in
exposure time and prediction coefficients corresponding to the respective
other pixels; and a motion amount identifying part identifying a motion
amount of the target pixel per unit time based on the calculated
prediction value of the target pixel and a value of the target pixel.
7. A recording medium in which the program according to claim 6 is
8. A learning apparatus comprising: a blur image generation part
generating a blur image obtained by adding motion blur corresponding to a
plurality of preset patterns of motion to an image captured by an image
capturing part capturing images using an image sensor configured by
times; and a coefficient calculation part calculating, corresponding to
the respective plurality of patterns of motion, prediction coefficients
for calculating a prediction value of the target pixel in the captured
image based on values of a plurality of other pixels different from the
target pixel in exposure time.
9. The learning apparatus according to claim 8, further comprising: a
prediction expression generation part generating a prediction expression
for predicting a value of the target pixel based on the values of the
plurality of other pixels in each blur image, wherein the coefficient
calculation part calculates values of coefficients by which the values of
the plurality of other pixels are multiplied in the generated prediction
expression as the prediction coefficients.
10. The learning apparatus according to claim 8, further comprising: a
storage part storing the calculated prediction coefficients in
association with the plurality of patterns of motion and positions of the
plurality of other pixels.
11. A learning method comprising: generating, with a blur image
generation part, a blur image obtained by adding motion blur
corresponding to a plurality of preset patterns of motion to an image
plurality of exposure times; and calculating, with a coefficient
calculation part, corresponding to the respective plurality of patterns
of motion, prediction coefficients for calculating a prediction value of
the target pixel in the captured image based on values of a plurality of
other pixels different from the target pixel in exposure time.
12. A program causing a computer to function as a learning apparatus
comprising: a blur image generation part generating a blur image obtained
by adding motion blur corresponding to a plurality of preset patterns of
motion to an image captured by an image capturing part capturing images
using an image sensor configured by regularly arranging a plurality of
pixels having a plurality of exposure times; and a coefficient
calculation part calculating, corresponding to the respective plurality
of patterns of motion, prediction coefficients for calculating a
prediction value of the target pixel in the captured image based on
13. A recording medium in which the program according to claim 12 is
stored.Description:
[0001] The present technology relates to an image processing apparatus and
method, a learning apparatus and method, a program and a recording
medium, and specifically relates to an image processing apparatus and
method, a learning apparatus and method, a program and a recording medium
capable of detecting motion of an image captured using an image sensor
with different exposure times readily in high accuracy.
[0002] A solid state image sensor such as a CCD is employed as an image
sensor used for image capturing apparatuses such as a video camera.
However, the image capturing apparatuses using the solid state image
sensor have a narrower dynamic range to quantity of incident light
compared with silver salt-type image capturing apparatuses. The narrow
dynamic range of the image capturing apparatuses can cause blocked up
shadows (underexposure) or blown out highlights (overexposure) in the
[0003] In related art, it is known that some image capturing apparatus can
extend its dynamic range by synthesizing the image with a wide dynamic
range using plural image signals under different exposure quantities.
Such past image capturing apparatus calculates a proper exposure quantity
based on the image signal captured by the image sensor at the first frame
and the exposure quantity at that time. Then, it performs the capturing
by the image sensor at the second frame based on this under the proper
exposure quantity or overexposure and underexposure. Next, it stores the
image signals at the first and second frames in a memory, and synthesizes
the image signals at the first and second frames stored in the memory to
generate one image with an extended dynamic range.
[0004] A technology is also proposed in which the image sensor is
constituted of two pixel groups into which all the pixels are divided, is
capable of reading out video signals with different exposure times from
the respective two pixel groups at one frame, and exchanges the exposure
times for the two pixel groups every one frame (for example, see Japanese
Patent Application Publication No. 2007-221423 which is hereinafter
referred to as Patent Document 1).
[0005] Moreover, detection of a motion amount of the image is important,
for example, when realizing an image stabilizing function for the video
camera and the like. In the past, the motion amount was detected using
chronologically sequential two images. When the motion amount is detected
in this manner, configuring the different exposure times for the two
images, for example, like Patent Document 1 can cause deterioration of
[0006] Therefore, it is also proposed that plural pixel groups are
integrated into one high-definition pixel group, and that plural times of
the capturing are performed sequentially under different capturing
conditions each time, for the purpose that the image capturing apparatus
capable of extending the dynamic range enhances the detection accuracy of
the motion amount (for example, see Japanese Patent Application
Publication No. 2010-219940 which is hereinafter referred to as Patent
[0007] According to the technology of Patent Document 2, the capturing can
be performed with different exposure times for each pixel group of the
image sensor by one-time exposure. For example, the pixel group of face A
and the pixel group of face B can start the exposure simultaneously and
the pixel group of face A can complete the exposure after the pixel group
of face B completes the exposure.
[0008] However, the detection of the motion amount, for example, according
to the technology of Patent Document 2, can still cause the deterioration
of the detection accuracy when the target pixel moves toward the pixel
different from itself in exposure time. The motion detection, for
example, using a block matching method or a gradient method leads to
difficulty of the difference extraction between the pixel exposed for a
longer time and the pixel exposed for a shorter time.
[0009] The present technology is disclosed in view of aforementioned
circumstances, and it is desirable to detect the motion of the image
captured by the image sensor with different exposure times readily in
[0010] According to a first aspect of the present technology, there is
provided an image processing apparatus including: a calculation part
[0011] The image processing apparatus can further include a coefficient
supply part supplying the prediction coefficients to the calculation
part, wherein the coefficient supply part supplies the prediction
coefficients corresponding to a preset pattern of motion to the
calculation part, and the calculation part calculates the prediction
value of the target pixel for each pattern of motion using a prediction
expression based on the values of the plurality of other pixels and the
prediction coefficients corresponding to the respective other pixels.
[0012] The motion amount identifying part can be configured to identify
the motion amount of the target pixel per unit time based on a prediction
error between the prediction value of the target pixel calculated for
each preset pattern of motion and the value of the target pixel.
[0013] The prediction coefficients can be prediction coefficients
previously learned by a learning apparatus, and the learning apparatus
can include: a blur image generation part generating a blur image
obtained by adding motion blur corresponding to a plurality of preset
patterns of motion to the image captured by the image sensor; and a
coefficient calculation part calculating, corresponding to the respective
plurality of patterns of motion, the prediction coefficients for
calculating the prediction value of the target pixel in the captured
image based on the values of the plurality of other pixels different from
the target pixel in exposure time.
[0014] According to the first aspect of the present technology, there is
provided an image processing method including: calculating, with a
calculation part, a prediction value of a target pixel in an image
coefficients corresponding to the respective other pixels; and
identifying, with a motion amount identifying part, a motion amount of
the target pixel per unit time based on the calculated prediction value
of the target pixel and a value of the target pixel.
[0015] According to the first aspect of the present technology, there is
provided a program causing a computer to function as an image processing
apparatus including: a calculation part calculating a prediction value of
a target pixel in an image captured by an image capturing part capturing
images using an image sensor configured by regularly arranging a
plurality of pixels having a plurality of exposure times based on values
of a plurality of other pixels different from the target pixel in
[0016] In the first aspect of the present technology, calculated is a
prediction value of a target pixel in an image captured by an image
to the respective other pixels; and identified is a motion amount of the
target pixel per unit time based on the calculated prediction value of
the target pixel and a value of the target pixel.
[0017] According to a second aspect of the present technology, there is
provided a learning apparatus including: a blur image generation part
[0018] The learning apparatus can further include a prediction expression
generation part generating a prediction expression for predicting a value
of the target pixel based on the values of the plurality of other pixels
in each blur image, wherein the coefficient calculation part calculates
values of coefficients by which the values of the plurality of other
pixels are multiplied in the generated prediction expression as the
prediction coefficients.
[0019] The learning apparatus can further include a storage part storing
the calculated prediction coefficients in association with the plurality
of patterns of motion and positions of the plurality of other pixels.
[0020] According to the second aspect of the present technology, there is
provided a learning method including: generating, with a blur image
[0021] According to the second aspect of the present technology, there is
provided a program causing a computer to function as a learning apparatus
including: a blur image generation part generating a blur image obtained
[0022] In the second aspect of the present technology, generated is a blur
image obtained by adding motion blur corresponding to a plurality of
preset patterns of motion to an image captured by an image capturing part
a plurality of pixels having a plurality of exposure times; and
calculated are, corresponding to the respective plurality of patterns of
motion, prediction coefficients for calculating a prediction value of the
target pixel in the captured image based on values of a plurality of
[0023] According to the present technology, the motion of the image
captured by the image sensor with different exposure times can be
detected readily in high accuracy.
[0024] FIG. 1 is a block diagram illustrating an example of a
configuration according to one embodiment of an image capturing control
system to which the present technology is applied;
[0025] FIG. 2 is a diagram illustrating an example of a configuration of a
light receiving plane of the image sensor in FIG. 1;
[0026] FIG. 3 is a diagram illustrating an example of a configuration of a
target pixel;
[0027] FIG. 4 is a diagram illustrating another example of the
configuration of the target pixel;
[0028] FIG. 5 is a diagram illustrating display in a polar coordinate
[0029] FIG. 6 is a block diagram illustrating a detailed example of a
configuration of a coefficient calculation part in FIG. 1;
[0030] FIG. 7 is a block diagram illustrating a detailed example of a
configuration of a motion amount detection part in FIG. 1;
[0031] FIG. 8 is a diagram for explaining selection of the minimum
prediction error by a minimum value selection part in FIG. 7;
[0032] FIG. 9 is a flowchart illustrating an example of coefficient
[0033] FIG. 10 is a flowchart illustrating an example of motion amount
[0034] FIG. 11 is a diagram illustrating another example of the
configuration of the light receiving plane of the image sensor in FIG. 1;
[0035] FIG. 12 is a block diagram illustrating an example of a
configuration of a personal computer.
[0036] Hereinafter, preferred embodiments of the present disclosure will
be described in detail with reference to the appended drawings. Note
that, in this specification and the appended drawings, structural
elements that have substantially the same function and structure are
denoted with the same reference numerals, and repeated explanation of
these structural elements is omitted.
[0037] FIG. 1 is a block diagram illustrating an example of a
system to which the present technology is applied. This image capturing
control system is configured to include an image capturing apparatus 11
constituted of, for example, a digital camera (digital still camera) or
the like and a learning apparatus 12.
[0038] The image capturing apparatus 11 in FIG. 1 is configured to include
an operation part 20, an image capturing part 41, an SDRAM (Synchronous
Dynamic Random Access Memory) 54, a motion amount detection part 55, a
correction part 57, a display control part 60 and a display part 61.
[0039] The operation part 20 is configured to include, for example, a
release switch 21, a touch panel overlapping with the display part 61
mentioned below, and the like, and is operated by a user. The operation
part 20 supplies an operation signal in response to the operation of the
user to an appropriate block of the image capturing apparatus 11.
[0040] The image capturing part 41 captures an image of a subject by
performing photoelectric conversion of received light incident thereinto,
and supplies the resulting captured image to the SDRAM 54 to cause it to
store (temporarily).
[0041] At this point, the image capturing part 41 is configured to include
an imaging lens 51, an image sensor 52 and a camera signal processing
part 53, and the imaging lens 51 forms the image of the subject on a
light receiving plane of the image sensor 52.
[0042] The image sensor 52 is configured to include, for example, a CCD
(Charge Coupled Devices) sensor, a CMOS (Complementary Metal Oxide
Semiconductor) sensor, or the like. The image sensor 52 supplies the
image (light) of the subject formed on its light receiving plane to the
camera signal processing part 53 as an analog image signal by the
photoelectronic conversion. In addition, a detailed example of a
configuration of the image sensor 52 will be described below.
[0043] The camera signal processing part 53 performs, for example, gamma
correction processing and/or white balance processing on the analog image
signal supplied from the image sensor 52. After that, the camera signal
processing part 53 performs A/D (Analog/Digital) conversion on the analog
image signal, and supplies the resulting digital image signal (captured
image) to the SDRAM 54 to cause it to store therein.
[0044] The SDRAM 54 stores the captured image supplied from the camera
signal processing part 53 (image capturing part 41).
[0045] The motion amount detection part 55 reads out the captured image
captured by the image capturing part 41 from the SDRAM 54. The motion
amount detection part 55 detects a motion amount regarding the captured
image read out from the SDRAM 54. The motion amount detection part 55
generates prediction expressions for predicting a value of a target pixel
using values of pixels around the target pixel and coefficients stored in
a coefficient storage part 83 of the learning apparatus 12 mentioned
below, and detects the motion amount based on an error (prediction error)
between the prediction value and an observed value of the target pixel.
[0046] The correction part 57 corrects the captured image supplied from
the motion amount detection part 55 based on the motion amount of the
captured image supplied from the same motion amount detection part 55,
and supplies the captured image after the correction to the display
control part 60.
[0047] The display control part 60 supplies the captured image supplied
from the correction part 57 to the display part 61 to cause it to
[0048] According to the control of the display control part 60, the
display part 61 displays the captured image and the like. For example, an
LCD (Liquid Crystal Display) or the like can be employed as the display
[0049] The learning apparatus 12 in FIG. 1 is configured to include a
pixel value acquisition control part 81, a coefficient calculation part
82 and the coefficient storage part 83.
[0050] The pixel value acquisition control part 81 controls acquisition of
values of predetermined pixels in the image data inputted into the
learning apparatus 12.
[0051] The coefficient calculation part 82 calculates coefficients
regarding motion prediction mentioned below.
[0052] The coefficient storage part 83 stores the coefficients calculated
by the coefficient calculation part 82 and supplies the coefficients to
the image capturing apparatus 11 as needed.
[0053] The learning apparatus 12 is configured to learn the coefficients
used for the prediction expressions for predicting the pixel value as
mentioned below, for example, receiving data of a still image (image
data) captured by the image capturing apparatus 11.
[0054] The pixel value acquisition control part 81 acquires the pixel
value of the target pixel and ones around the target pixel in the image
data supplied to the learning apparatus 12. As mentioned below, images
having motion blur corresponding to a plurality of patterns of motion are
generated. Then, based on the generated images, the prediction
expressions, each corresponding to each motion, are generated. The values
of the coefficients used for the prediction expression are calculated,
for example, using a least square method or the like. These constitute
the learning of the coefficients by the learning apparatus 12.
[0055] The coefficients obtained by the learning are stored in the
coefficient storage part 83 and supplied to the motion amount detection
part 55 of the image capturing apparatus 11.
[0056] FIG. 2 is a diagram illustrating a detailed example of a
configuration of the image sensor 52 in FIG. 1 as an example of a
configuration of the light receiving plane. As illustrated in the figure,
pixels with a longer exposure time and pixels with a shorter exposure
time are regularly arranged in the imaging plane of the image sensor.
Herein, the pixels with the longer exposure time are referred to as
longer accumulation pixels on the basis that they accumulate charge
obtained by the photoelectric conversion for a longer time, and
represented by a symbol `Lx` in the figure, where x as a suffix denotes a
natural number. Also, the pixels with the shorter exposure time are
referred to as shorter accumulation pixels on the basis that they
accumulate charge obtained by the photoelectric conversion for a shorter
time, and represented by a symbol `sx` in the figure, where x as a suffix
denotes a natural number.
[0057] In the example of FIG. 2, 25 (5×5) pixels are arranged into a
square shape, and the longer accumulation pixels and the shorter
accumulation pixels are arranged alternately therein. In this example, 13
longer accumulation pixels and 12 shorter accumulation pixels are
arranged. Although the number of the pixels arranged in the image sensor
52 is 25 for simplicity, more pixels are arranged practically.
[0058] The image capturing apparatus 11 is configured to capture images
with a wide dynamic range by using the image sensor 52 as illustrated in
[0059] Next, the learning of the coefficients by the learning apparatus 12
[0060] For example, image data of a still image captured by the image
sensor 52 as illustrated in FIG. 2 as image data for the learning is
prepared, and a target pixel in the image represented by the image data
[0061] For example, a pixel L12, which is the pixel indicated by the
thick-bordered box, illustrated in FIG. 3 is configured as the target
pixel. In this case, it is expected that the pixel L12, which is the
target pixel in the image data, has a pixel value obtained corresponding
to charge accumulated in the pixel L12 as a longer accumulation pixel
constituting the image sensor 52.
[0062] Herein, for example, when it is assumed that the target pixel moves
by a motion amount mx in a horizontal direction and a motion amount my in
a vertical direction, an image with motion blur (referred to as a blur
image) obtained corresponding to the motion amounts is to be generated. A
motion amount (mx, my) is defined as a vector representing a distance by
which the subject moves in a unit time in the horizontal direction (x
axis direction) and the vertical direction (y axis direction) in pixel
[0063] When the subject moves during exposure of pixels in the image
sensor, light corresponding to one pixel in the still image of the
subject is accumulated in plural pixels and thus the motion blur arises.
Meanwhile, pixel values of the blur image can be generated, for example,
by displacing the individual pixels in the still image according to the
motion amount (mx, my) in the horizontal or vertical direction, adding
the pixel values obtained by the displacement and the original pixel
values to normalize, and the like. In addition, when generating the pixel
values of the blur image, it is considered that the image sensor
illustrated in FIG. 3 includes the longer accumulation pixels and the
shorter accumulation pixels. That is, the pixel values are generated,
taking into account of a speed specified corresponding to the motion
amount and exposure times for the individual pixels.
[0064] For example, it is assumed that there are 5 motions in the
horizontal direction and 5 motions in the vertical direction, and thus,
25 motion amounts (mx, my) (25 patterns) totally. For example, the motion
amounts such as (-2, -2), (-2, -1), . . . , and (2, 2) can be assumed.
Corresponding to these plural patterns of motion, the respective blur
[0065] In the learning of the coefficients by the learning apparatus 12,
at first, the blur images corresponding to the plural patterns of motion
are generated as above.
[0066] After obtaining the blur images as mentioned above, a prediction
expression is generated for predicting the pixel value of the pixel L12
as the longer accumulation pixel based on a pixel s1, pixel s3, . . . ,
and pixel s23 as the shorter accumulation pixels. In this case, Equation
(1) is generated as the prediction expression for predicting the pixel
value of the pixel L12 based on the pixel values of the 12 shorter
accumulation pixels.
[ Expression 1 ] L 12 = k = 0 11
ω s → L , mx , my , 2 k + 1 * s 2
k + 1 + e s → L , mx , my , L 12 ( 1 )
[0067] Herein, the coefficients in Equation (1) are represented by
ωs→L, mx, my, 2k+1=ω (2)
[0068] The coefficients w in Equation (1) represent the coefficients for
calculating the pixel value of the longer accumulation pixel based on the
pixel values of the shorter accumulation pixels when a motion amount (mx,
my) is given, that is, the coefficients by which the pixel values of the
shorter accumulation pixels whose suffixes are 2k+1, where k is an
integer of 0 to 11, are multiplied. In other words, Equation (1) is for
predicting the target pixel value by calculating the value of the pixel
L12 using a linear expression for the total sum of the individual values
obtained by multiplication of taps by the coefficients ω, where the
taps are the values of the 12 shorter accumulation pixels existing around
the pixel L12.
[0069] Moreover, Equation (3) represents the rightmost term on the right
hand side of Equation (1).
es→L, mx, my=e (3)
[0070] The term e on the right hand side of Equation (1) represents a
prediction error in calculating (predicting) the pixel value of the
longer accumulation pixel L12 based on the pixel values of the shorter
accumulation pixels when a motion amount (mx, my) is given.
[0071] Generating sets of Equation (1) and Equation (3) as samples from a
plurality of image data inputted into the learning apparatus 12 enables
calculation of the coefficients for which the prediction error is at its
minimum in Equation (1), for example, using a least square method. Thus,
the coefficients can be calculated for the multiplication of the pixel
s1, pixel s3, . . . , and pixel s23, respectively. For example, 12
coefficients are calculated for one motion amount (mx, my). And
similarly, sets of the 12 coefficients are calculated, for example, for
25 motion amounts (mx, my), respectively.
[0072] By doing this, obtained are the coefficients for calculating the
pixel value of the longer accumulation pixel based on the pixel values of
the shorter accumulation pixels, that is, the sets of the coefficients,
for example, corresponding to the 25 motion amounts (mx, my).
[0073] Next, in the same manner as in the above-mentioned case,
coefficients for predicting a pixel value of a shorter accumulation pixel
based on pixel values of longer accumulation pixels are evaluated.
[0074] That is, image data of a still image, for example, captured by the
image sensor 52 as illustrated in FIG. 4 as image data for the learning
is prepared, and a target pixel in the image represented by the image
data is configured.
[0075] For example, a pixel s12, which is the pixel indicated by the
thick-bordered box, illustrated in FIG. 4 is configured as the target
pixel. In this case, it is expected that the pixel s12, which is the
to charge accumulated in the pixel s12 as a shorter accumulation pixel
[0076] Then, as in the above-mentioned case, the blur images corresponding
to the plural patterns of motion are generated.
[0077] After obtaining the blur images, a prediction expression is
generated for predicting a pixel value of the pixel s12 as the shorter
accumulation pixel based on a pixel L1, pixel L3, . . . , and pixel L23
as the longer accumulation pixels. In this case, Equation (4) is
generated as the prediction expression for predicting the pixel value of
the pixel s12 based on the pixel values of the 12 longer accumulation
[ Expression 4 ] s 12 = k = 0 11
ω L → s , mx , my , 2 k + 1 * L 2
k + 1 + e L → s , mx , my ( 4 ) ##EQU00002##
[0078] Herein, the coefficients in Equation (4) are represented by
ωL→s, mx, my, 2k+1=ω (5)
[0079] The coefficients ω in Equation (4) represent the coefficients
for calculating the pixel value of the shorter accumulation pixel based
on the pixel values of the longer accumulation pixels when a motion
amount (mx, my) is given, that is, the coefficients by which the pixel
values of the longer accumulation pixels whose suffixes are 2k+1, where k
is an integer of 0 to 11, are multiplied. In other words, Equation (4) is
for predicting the target pixel value by calculating the value of the
pixel s12 using a linear expression for the total sum of the individual
values obtained by multiplication of taps by the coefficients ω,
where the taps are the values of the 12 longer accumulation pixels
existing around the pixel s12.
[0080] Moreover, Equation (6) represents the rightmost term on the right
hand side of Equation (4).
eL→s, mx, my=e (6)
[0081] The term e in Equation (4) represents a prediction error in
calculating the pixel value of the shorter accumulation pixel s12 based
amount (mx, my) is given.
[0082] The coefficients for which the prediction error is at its minimum
in Equation (4), for example, using a least square method can be
calculated. Thus, the coefficients are calculated for the multiplication
of the pixel L1, pixel L3, . . . , and pixel L23, respectively. For
example, 12 coefficients are calculated for one motion amount (mx, my).
And similarly, sets of the 12 coefficients are calculated, for example,
for 25 motion amounts (mx, my), respectively.
[0083] By doing this, obtained are the coefficients for calculating the
pixel value of the shorter accumulation pixel based on the pixel values
of the longer accumulation pixels, that is, the sets of the coefficients,
[0084] As mentioned above, detection of the motion amounts by the motion
amount detection part 55 is performed using the coefficients learned by
the learning apparatus 12 (coefficients stored in the coefficient storage
part 83). Next, the detection of the motion amounts by the motion amount
detection part 55 is described in detail.
[0085] A pixel for which the motion amounts are to be detected is
configured as the target pixel in image data, for example, supplied from
the SDRAM 54. Then, the prediction expression for predicting the value of
the target pixel is generated based on the values of the pixels around
the target pixel using the coefficients learned by the learning apparatus
12. Herein, the prediction expression is generated for each of the plural
patterns of motion.
[0086] When the target pixel is the longer accumulation pixel, Equation
(7) as the prediction expression is generated, for example, for each of
the 25 motion amounts (mx, my).
[ Expression 7 ] L 12 , mx , my ' = k = 0
11 ω s → L , mx , my , 2 k + 1 * s
2 k + 1 ( 7 ) ##EQU00003##
[0087] When L12 represents an observed value of the target pixel in
the image data supplied from the SDRAM 54, Equation (8) represents the
prediction error for Equation (7).
es→L, mx, my=L12-L12, mx, my' (8)
[0088] As mentioned above, since the prediction expression of Equation (7)
is generated for each of the plural patterns of motion, the prediction
error represented by Equation (8) is obtained also for each of the plural
patterns of motion. For example, 25 prediction errors are obtained.
[0089] Accordingly, when the prediction error whose absolute value is at
its minimum is selected, for example, from among the 25 prediction
errors, the motion amount corresponding to the selected one is considered
closest to the motion of the target pixel in the image data supplied from
the SDRAM 54. The motion amount detection part 55 selects the prediction
error whose absolute value is at its minimum, for example, from among the
25 prediction errors to output the motion amount (mx, my) corresponding
to this one as the detection result.
[0090] Or the motion amount detection part 55 may calculate the motion
amounts for the respective plural pixels adjacent to the target pixel
similarly, and output the motion amount obtained by normalization of
these motion amounts or the motion amount decided by majority as the
[0091] On the other hand, when the target pixel is the shorter
accumulation pixel,
[0092] Equation (9) as the prediction expression is generated for each of
[ Expression 9 ] s 12 , mx , my ' = k = 0
11 ω L → s , mx , my , 2 k + 1 * L
2 k + 1 ( 9 ) ##EQU00004##
[0093] Then, same as for Equation (8) in the case of the longer
accumulation pixel, the calculation of the prediction error for Equation
(9) enables the detection of the motion amount which is considered
closest to the motion of target pixel in the image data supplied from the
SDRAM 54
[0094] In the above argument, although an example in the case that the 25
motion amounts (mx, my) are assumed is described, the patterns of motion
are, of course, not limited to those.
[0095] Thus, the motion amount is detected.
[0096] In the above argument, although an example in the case that the (x,
y) coordinate system, that is, orthogonal coordinate system is used for
identifying the pixel position is described, the (r, θ) coordinate
system as a polar coordinate system can be used.
[0097] When the polar coordinate system is used, a desired pixel position
can be represented by a radius r of a circle whose center is identical to
the origin (0, 0) in the orthogonal coordinate system and the angle 0
formed by the line connecting a point on the circumference of the circle
and the origin and an X axis as illustrated in FIG. 5. In other words,
the orthogonal coordinate system and the polar coordinate system can be
converted to each other using Equation (10) and Equation (11).
[ Expression 10 ] ( x y ) = ( r
cos θ r sin θ ) ( 10 )
[ Expression 11 ] ( r θ ) = (
x 2 + y 2 tan - 1 ( y x ) ) ( 11 )
[0098] FIG. 6 is a block diagram illustrating a detailed example of a
configuration of the coefficient calculation part 82 in FIG. 1. As
illustrated in the figure, the coefficient calculation part 82 is
configured to include a motion blur image generation part 101, a
prediction expression generation part 102 and an operation processing
[0099] For example, when it is assumed that the target pixel moves by a
motion amount mx in the horizontal direction and a motion amount my in
the vertical direction, the motion blur image generation part 101
generates a blur image obtained corresponding to the motion amounts.
Herein, the motion blur image generation part 101 is configured to
include a plurality of image generation portions inside, and in this
example, configured to include an image generation portion of H0V0, an
image generation portion of H1V1, . . . , and an image generation portion
of H4V4.
[0100] As mentioned above, the motion blur image generation part 101
generates blur images corresponding to the plural patterns of motion. For
example, it is assumed that there are 5 motions in the horizontal
direction and 5 motions in the vertical direction, and thus, 25 motion
amounts (mx, my) (25 patterns) totally to generate the blur images. For
example, the motion amounts such as (-2, -2), (-2, -1), . . . , and (2,
2) can be assumed. Corresponding to the motions in the horizontal
direction (H) and the motions in the vertical direction (V) in these
plural patterns of motion, the image generation portion of H0V0, the
image generation portion of H1V1, . . . , and the image generation
portion of H4V4 generate the blur images, respectively.
[0101] The prediction expression generation part 102 generates the
prediction expressions, for example, as illustrated in Equation (1) or
Equation (4). In the prediction expression generation part 102,
expression generation portions are provided to generate the prediction
expressions corresponding to the respective blur images generated by the
motion blur image generation part 101. The expression generation portions
of the prediction expression generation part 102 generate the respective
prediction expressions, for example, corresponding to the 25 motion
amounts (mx, my).
[0102] The operation processing part 103 calculates the coefficients for
which the prediction error is at its minimum in Equation (1) or Equation
(4), for example, using a least square method. Thereby, the coefficients
are calculated, for example, for the multiplication of the pixel s1,
pixel s3, . . . , and pixel s23 in Equation (1), respectively. In other
words, the coefficients are calculated corresponding to the positions of
the plural pixels as the taps, respectively. For example, the 12
similarly, the sets of the 12 coefficients are calculated, for example,
for the respective 25 motion amounts (mx, my).
[0103] Thus, the coefficients calculated by the operation processing part
103 are to be stored in the coefficient storage part 83. In other words,
the coefficient storage part 83 stores the coefficients calculated by the
operation processing part 103 in association with the plural patterns of
motion (for example, 25 motion amounts) and the positions of the pixels
as the taps.
[0104] FIG. 7 is a block diagram illustrating a detailed example of a
configuration of the motion amount detection part 55 in FIG. 1.
[0105] In this example, the motion amount detection part 55 is configured
to include a pixel value acquisition control part 201, a prediction error
calculation part 202, a minimum value selection part 203, a motion amount
identifying part 204 and a coefficient supply part 205.
[0106] The pixel value acquisition control part 201 controls acquisition
of the values of the predetermined pixels in the image data inputted into
the motion amount detection part 55.
[0107] The prediction error calculation part 202 calculates the prediction
errors as illustrated in Equation (6) or Equation (8). Herein, the
prediction error calculation part 202 is configured to include a
plurality of calculation portions inside, and in this example, configured
to include a calculation portion of H0V0, a calculation portion of H1V1,
. . . , and a calculation portion of H4V4.
[0108] As mentioned above, the prediction error calculation part 202
calculates the 25 prediction errors obtained corresponding to the
respective plural patterns of motion. Corresponding to the motions in the
horizontal direction (H) and the motions in the vertical direction (V) in
these plural patterns of motion, the calculation part of H0V0, the
calculation part of H1V1, . . . , and the calculation part of H4V4
calculate the prediction errors, respectively.
[0109] The coefficient supply part 205 is configured to acquire the
coefficients stored in the coefficient storage part 83 of the learning
apparatus 12 and supply the coefficients to the prediction error
calculation part 202 as needed. For example, when the prediction error is
calculated in the case that the motion amount is (-2, -2), for example,
the 12 coefficients are supplied corresponding to the motion amount.
Moreover, when the prediction error is calculated in the case that the
motion amount is (-2, -1), for example, the 12 coefficients are supplied
corresponding to the motion amount. Thus, the sets of the coefficients
are supplied, for example, corresponding to the respective 25 patterns.
[0110] The minimum value selection part 203 selects the prediction error
whose absolute value is at its minimum from among the plural ones
calculated by the prediction error calculation part 202. As mentioned
above, the motion amount corresponding to the selected prediction error
is considered closest to the motion of the target pixel in the image data
supplied from the SDRAM 54.
[0111] The motion amount identifying part 204 identifies the motion amount
corresponding to the prediction error selected by the minimum value
selection part 203 to output the motion amount.
[0112] FIG. 8 is a diagram for explaining the selection of the minimum
prediction error by the minimum value selection part 203. In the figure,
the vertical axis represents the reciprocal number of the prediction
error (referred to as PSNR), the horizontal axis represents θ, and
variations of the values of PSNR corresponding to the 5 values of r are
plotted. In the figure, the variations of the values of PSNR are plotted
with different symbols corresponding to the values of r, respectively.
[0113] In the example in the figure, the value of PSNR is the highest at
the point surrounded by a circle 301 (a triangle in the figure), and the
prediction error is at its minimum at this point. Accordingly, the motion
amount which is a motion amount represented by (r, θ) in the polar
coordinate system, and for which the value of r is the value
corresponding to the point plotted with the triangle in the figure and
the value of θ is approximately 45, is considered closest to the
motion of the target pixel in the image data supplied from the SDRAM 54.
[0114] Or the motion amounts may be calculated for the respective plural
pixels adjacent to the target pixels similarly to output the motion
amount obtained by normalization of these motion amounts or the motion
amount decided by majority as the detection result.
[0115] For example, the detection of the motion amount using the past
technology tends to cause deterioration of detection accuracy, when the
target pixel moves toward the pixel different from itself in exposure
time. Moreover, the motion detection, for example, using a block matching
method or a gradient method leads to difficulty of the difference
extraction between the pixel exposed for a longer time and the pixel
exposed for a shorter time.
[0116] In contrast, according to the present technology, even when the
time, the deterioration of the detection accuracy does not necessarily
arise. Employing the present technology, there is no need for the
extraction of the difference between chronologically sequential frames,
and therefore, the motion can be detected readily and quickly.
[0117] Hence, according to the present technology, the motion of the image
[0118] Next, an example of coefficient learning processes by the learning
apparatus 12 in FIG. 1 are described, referring to a flowchart in FIG. 9.
[0119] In step S21, input of the image is accepted.
[0120] In step S22, the target pixel in the image whose input is accepted
in the process of step S21 is configured.
[0121] In step S23, the motion blur image generation part 101 generates
the blur image obtained corresponding to the motion amount, for example,
when it is assumed that the target pixel moves by the motion amount mx in
the horizontal direction and the motion amount my in the vertical
[0122] At this stage, the motion blur image generation part 101 generates
the blur images corresponding to the plural patterns of motion as
mentioned above. For example, assuming that there are 5 motions in the
25 motion amounts (mx, my) (25 patterns) totally, the blur images are
generated. For example, the motion amounts such as (-2, -2), (-2, -1), .
. . , and (2, 2) can be assumed. Corresponding to the motions in the
these plural patterns of motion, the image generation portion of H0V0,
the image generation portion of H1V1, . . . , and the image generation
portion of H4V4 generate the respective blur images.
[0123] In step S24, the prediction expression generation part 102
generates the prediction expressions, for example, as indicated in
Equation (1) or Equation (4). At this stage, the expression generation
portions of the prediction expression generation part 102 generate the
respective prediction expressions, for example, corresponding to the 25
motion amounts (mx, my).
[0124] In step S25, the operation processing part 103 calculates the
coefficients in the prediction expressions generated in step S24. At this
stage, the operation processing part 103 calculates the coefficients for
pixel s3, . . . , and pixel s23 in Equation (1), respectively. For
example, the 12 coefficients are calculated for one motion amount (mx,
my). And similarly, the sets of the 12 coefficients are calculated, for
example, for the respective 25 motion amounts (mx, my).
[0125] In step S26, the coefficient storage part 83 stores the
coefficients calculated in the process of step S25.
[0126] Thus, the coefficient learning processes have been performed.
[0127] Next, an example of the motion amount detection processes by the
motion amount detection part 55 in FIG. 7 are described, referring to a
flowchart in FIG. 10. Prior to these processes, it is assumed that the
coefficient supply part 205 acquires the coefficients stored in the
coefficient storage part 83 of the learning apparatus 12.
[0128] In step S41, input of the image is accepted.
[0129] In step S42, the target pixel in the image whose input is accepted
in the process in step S41 is configured.
[0130] In step S43, the coefficient supply part 205 supplies the
coefficients to the prediction error calculation part 202 as needed. In
other words, the coefficients, for example, corresponding to the
respective 25 patterns are supplied for the operation of the prediction
error in the process of step S44 mentioned below.
[0131] In step S44, the prediction error calculation part 202 calculates
the prediction error as indicated in Equation (6) or Equation (8). At
this stage, the prediction error calculation part 202 calculates the 25
prediction errors corresponding to the respective plural patterns of
motion as mentioned above.
[0132] Corresponding to the motions of horizontal direction (H) and the
motions of vertical direction (V) in these plural patterns of motion, the
calculation portion of H0V0, the calculation portion of H1V1, . . . , and
the calculation portion of H4V4 calculate the prediction errors,
respectively, and at this stage, the coefficients supplied in the process
of step S43 are used, respectively.
[0133] In step S45, the minimum value selection part 203 selects the
prediction error whose absolute value is at its minimum from among the
plural ones calculated in the process of step S44.
[0134] In step S46, the motion amount identifying part 204 identifies the
motion amount corresponding to the prediction error selected in the
process of step S45 as the motion amount of the target pixel.
[0135] In step S47, the motion amount identifying part 204 outputs the
motion amount identified in the process of step S46 as the detection
[0136] Thus, the motion amount detection processes have been performed.
[0137] Incidentally, although FIG. 2 to FIG. 4 illustrate the example in
which the longer accumulation pixels and the shorter accumulation pixels
are arranged in the imaging plane of the image sensor 52 one by one
alternately, the longer accumulation pixels and the shorter accumulation
pixels are not necessarily arranged one by one alternately.
[0138] For example, even when the light receiving plane of the image
sensor 52 is configured as illustrate in FIG. 11, the present technology
is, of course, applicable. FIG. 11 is a diagram illustrating a detailed
example of the configuration of the image sensor 52 in FIG. 1 as another
example of the configuration of the light receiving plane. In the example
in the figure, the shorter accumulation pixels are arranged every two
rows. Also in the figure, the longer accumulation pixels are represented
by a symbol `Lx` in the figure, where x as a suffix denotes a natural
number and the shorter accumulation pixels are represented by a symbol
`sx` in the figure, where x as a suffix denotes a natural number.
[0139] Namely, in the configuration in FIG. 11, as to the light receiving
plane of the image sensor 52 constituted of the pixels in 5 rows and 5
columns, the shorter accumulation pixels are arranged in the first row
thereof but the shorter accumulation pixels are not arranged in the
second row thereof. Moreover, the shorter accumulation pixels are
arranged in the third row thereof but the shorter accumulation pixels are
not arranged in the fourth row thereof. The shorter accumulation pixels
are arranged in the lowermost row thereof.
[0140] For example, when the target pixel is configured as the pixel
indicated by s12 in FIG. 11, Equation (12) can be used as the prediction
expression in place of Equation (9).
[ Expression 12 ] s 12 ' = t = 1 , 3 , 5
, 6 , 8 , 9 , 11 , 13 , 15 , 16 , 17 , 18 , 19 , 21 , 23 ω
L → s , mx , my , t * L t ( 12 ) ##EQU00006##
[0141] In addition, the numerical values indicated as `t=1, 3, 5, . . . ,
23` in Equation (12) represent the tap numbers, and the suffix of each of
the longer accumulation pixels in FIG. 11 is designated corresponding to
each value of t.
[0142] Thus, for example as illustrated in FIG. 11, even when using the
image sensor 52 in which the longer accumulation pixels and the shorter
accumulation pixels are arranged unevenly, the present technology can be
[0143] The series of processes described above can be realized by hardware
or software. When the series of processes is executed by the software, a
program forming the software is installed in a computer embedded in
dedicated hardware and a general-purpose personal computer 700
illustrated in FIG. 11 in which various programs can be installed and
various functions can be executed, through a network or a recording
[0144] In FIG. 12, a central processing unit (CPU) 701 executes various
processes according to a program stored in a read only memory (ROM) 702
or a program loaded from a storage unit 708 to a random access memory
(RAM) 703. In the RAM 703, data that is necessary for executing the
various processes by the CPU 701 is appropriately stored.
[0145] The CPU 701, the ROM 702, and the RAM 703 are connected mutually by
a bus 704. Also, an input/output interface 705 is connected to the bus
[0146] An input unit 706 that includes a keyboard and a mouse, an output
unit 707 that includes a display composed of a liquid crystal display
(LCD) and a speaker, a storage unit 708 that is configured using a hard
disk, and a communication unit 709 that is configured using a modem and a
network interface card such as a LAN card are connected to the
input/output interface 705. The communication unit 709 executes
communication processing through a network including the Internet.
[0147] A drive 710 is connected to the input/output interface 705
according to necessity, a removable medium 711 such as a magnetic disk,
an optical disc, a magneto optical disc, or a semiconductor memory are
appropriately mounted, and a computer program that is read from the
removable medium 711 is installed in the storage unit 708 according to
[0148] When the series of processes is executed by the software, a program
forming the software is installed through the network such as the
Internet or a recording medium composed of the removable medium 711.
[0149] The recording medium may be configured using the removable medium
711 illustrated in FIG. 12 that is composed of a magnetic disk (including
a floppy disk (registered trademark)), an optical disc (including a
compact disc-read only memory (CD-ROM) and a digital versatile disc
(DVD)), a magneto optical disc (including a mini-disc (MD) (registered
trademark)), or a semiconductor memory, which is distributed to provide a
program to a user and has a recorded program, different from a device
body, and may be configured using a hard disk that is included in the ROM
702 provided to the user in a state embedded in the device body in
advance having a recorded program or the storage unit 708.
[0150] In the present disclosure, the series of processes includes a
process that is executed in the order described, but the process is not
necessarily executed temporally and can be executed in parallel or
[0151] It should be understood by those skilled in the art that various
[0152] Additionally, the present technology may also be configured as
below. [0153] (1) An image processing apparatus comprising:
[0154] a calculation part calculating a prediction value of a target pixel
in an image captured by an image capturing part capturing images using an
image sensor configured by regularly arranging a plurality of pixels
having a plurality of exposure times based on values of a plurality of
other pixels different from the target pixel in exposure time and
prediction coefficients corresponding to the respective other pixels; and
[0155] a motion amount identifying part identifying a motion amount of the
the target pixel and a value of the target pixel. [0156] (2) The image
processing apparatus according to (1), further comprising:
[0157] a coefficient supply part supplying the prediction coefficients to
the calculation part, wherein
[0158] the coefficient supply part supplies the prediction coefficients
corresponding to a preset pattern of motion to the calculation part, and
[0159] the calculation part calculates the prediction value of the target
pixel for each pattern of motion using a prediction expression based on
the values of the plurality of other pixels and the prediction
coefficients corresponding to the respective other pixels. [0160] (3) The
image processing apparatus according to (1) or (2), wherein
[0161] the motion amount identifying part identifies the motion amount of
the target pixel per unit time based on a prediction error between the
prediction value of the target pixel calculated for each preset pattern
of motion and the value of the target pixel. [0162] (4) The image
processing apparatus according to any one of (1) to (4), wherein
[0163] the prediction coefficients are prediction coefficients previously
learned by a learning apparatus, and
[0164] the learning apparatus includes:
[0165] a blur image generation part generating a blur image obtained by
adding motion blur corresponding to a plurality of preset patterns of
motion to the image captured by the image sensor; and
[0166] a coefficient calculation part calculating, corresponding to the
respective plurality of patterns of motion, the prediction coefficients
for calculating the prediction value of the target pixel in the captured
the target pixel in exposure time. [0167] (5) An image processing method
[0168] calculating, with a calculation part, a prediction value of a
target pixel in an image captured by an image capturing part capturing
other pixels; and
[0169] identifying, with a motion amount identifying part, a motion amount
of the target pixel per unit time based on the calculated prediction
value of the target pixel and a value of the target pixel. [0170] (6) A
program causing a computer to function as an image processing apparatus
[0171] a calculation part calculating a prediction value of a target pixel
[0172] a motion amount identifying part identifying a motion amount of the
the target pixel and a value of the target pixel. [0173] (7) A recording
medium in which the program according to (6) is stored. [0174] (8) A
learning apparatus comprising:
[0175] a blur image generation part generating a blur image obtained by
pixels having a plurality of exposure times; and
[0176] a coefficient calculation part calculating, corresponding to the
respective plurality of patterns of motion, prediction coefficients for
calculating a prediction value of the target pixel in the captured image
based on values of a plurality of other pixels different from the target
pixel in exposure time. [0177] (9) The learning apparatus according to
(8), further comprising:
[0178] a prediction expression generation part generating a prediction
expression for predicting a value of the target pixel based on the values
of the plurality of other pixels in each blur image, wherein
[0179] the coefficient calculation part calculates values of coefficients
by which the values of the plurality of other pixels are multiplied in
the generated prediction expression as the prediction coefficients.
[0180] (10) The learning apparatus according to (8) or (9), further
[0181] a storage part storing the calculated prediction coefficients in
plurality of other pixels. [0182] (11) A learning method comprising:
[0183] generating, with a blur image generation part, a blur image
patterns of motion to an image captured by an image capturing part
[0184] calculating, with a coefficient calculation part, corresponding to
target pixel in exposure time. [0185] (12) A program causing a computer
to function as a learning apparatus comprising:
[0186] a blur image generation part generating a blur image obtained by
[0187] a coefficient calculation part calculating, corresponding to the
pixel in exposure time. [0188] (13) A recording medium in which the
program according to (12) is stored.
[0189] The present disclosure contains subject matter related to that
disclosed in Japanese Priority Patent Application JP 2011-155712 filed in
the Japan Patent Office on Jul. 14, 2011, the entire content of which is
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