CN106412421A - System and method for rapidly generating large-size multi-focused image - Google Patents
System and method for rapidly generating large-size multi-focused image Download PDFInfo
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- CN106412421A CN106412421A CN201610762750.1A CN201610762750A CN106412421A CN 106412421 A CN106412421 A CN 106412421A CN 201610762750 A CN201610762750 A CN 201610762750A CN 106412421 A CN106412421 A CN 106412421A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/80—Camera processing pipelines; Components thereof
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N5/00—Details of television systems
- H04N5/222—Studio circuitry; Studio devices; Studio equipment
- H04N5/262—Studio circuits, e.g. for mixing, switching-over, change of character of image, other special effects ; Cameras specially adapted for the electronic generation of special effects
- H04N5/2621—Cameras specially adapted for the electronic generation of special effects during image pickup, e.g. digital cameras, camcorders, video cameras having integrated special effects capability
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Abstract
The invention discloses a system and method for rapidly generating a large-size multi-focused image. The system comprises an image obtaining module and an image processing module; the image obtaining module is used for obtaining an original image; the image processing module comprises an image pre-processing sub-module, an image blurring sub-module and an image fusion sub-module; the image pre-processing sub-module is used for performing depth information calculation and down-sampling of the original image, so that a down-sampled original image and a down-sampled depth image are obtained; the image blurring sub-module is used for calculating a field depth range and a blurring coefficient, and performing blurring processing of the down-sampled original image by utilizing the blurring coefficient, so that a blurred image is obtained; and the image fusion sub-module is used for amplifying the blurred image to the size of the original image, and fusing the blurred image with the original image, so that a multi-focused image is obtained. According to the system and the method disclosed by the invention, for the down-sampled reduced image, calculation of the blurring coefficient and blurring processing are carried out; after being obtained, the blurred image is amplified to the size of the original image, and fused with the original image; and thus, the processing time is greatly reduced.
Description
Technical field
The present invention relates to a kind of System and method for quickly generating large scale weight focus image.
Background technology
With popular, the requirement more and more higher to mobile phone camera for the consumer that social activity is shared in recent years, not only to shoot
Pixel is higher, the image of better quality, also requires to have more expanded functions, or even is desirable to close to single anti-effect.Such as,
The different background blurring image of focusing can be shot, the afocal virtualization effect as butter（bokeh）Etc..
Although Dan Fanneng shoots background blurring image, once it is determined that focus, before and after image, scape also determines that
, the image focal point of generation also cannot be altered again；But most of consumer is not the Camera crews of specialty, is difficult to once
Property shoot satisfied image, they wish clap after the focus of image can also be modified（Focus again）, redefine image
Foreground and background, to reach satisfied effect.
Content of the invention
It is an object of the invention to overcoming the deficiencies in the prior art, a kind of large scale that quickly generates is provided to weigh focus image
System and method for, carries out blurring the calculating of coefficient for the image after down-sampled reducing and virtualization is processed, after obtaining blurring image,
It is amplified to original image size again to be merged with original image, greatly reduce process time.
The purpose of the present invention is achieved through the following technical solutions：A kind of large scale that quickly generates weighs focus image
System, including image collection module and image processing module：
Image collection module, for obtaining original image；
Image processing module, including：
Image semantic classification submodule, calculates and down-sampled pretreatment for original image is carried out with depth information, obtains down-sampled
Original image and down-sampled depth image；
Image blurs submodule, for calculating field depth and virtualization coefficient, and using virtualization coefficient, down-sampled original image is entered
Row virtualization is processed, and obtains blurring image；
Image co-registration submodule, for virtualization image is amplified to original image size, is merged with original image, obtains focusing figure again
Picture.
Further, a kind of described system quickly generating large scale weight focus image, also includes library module, is used for
Original image and weight focus image are stored.
Further, described image collection module includes at least two photographic head, for gathering at least with different view
Two width original images.
Further, described Image semantic classification submodule includes：
Depth information computing unit, for calculating the depth value of each of original image pixel according to principle of triangulation, obtains
To depth image；
Down-sampled unit, for reducing for original image and depth image by down-sampled mode, obtains down-sampled artwork
Picture and down-sampled depth image.
Further, described image virtualization submodule includes：
Field depth computing unit, calculates field depth for combining photographic head parameter according to down-sampled depth image；
Virtualization coefficient calculation unit, for calculating virtualization coefficient blurWeight to each of down-sampled original image pixel：
blurWeight = (d-T1)/(T2–T1)；Wherein, d is the depth value of current pixel point, and T1, T2 are parameter preset, use
In the slope controlling formula.
Virtualization processing unit, separates for image being carried out foreground and background according to virtualization coefficient：Virtualization coefficient is zero
Pixel represents prospect, and the pixel that virtualization coefficient is not zero represents background；And according to virtualization coefficient, background is carried out at virtualization
Reason, obtains blurring image.
Further, described image co-registration submodule includes：
Virtualization image enlarging unit, for virtualization image is amplified to original image size, obtains blurring enlarged drawing；
Integrated unit, for by blurring enlarged drawing prompting virtualization and clearly scope and virtualization degree, being then coupled to former
In image, obtain weight focus image.
A kind of method quickly generating large scale weight focus image, comprises the following steps：
S1. obtain original image using the image collection module at least with two photographic head；
S2. original image is carried out with depth information calculate and down-sampled pretreatment, obtain down-sampled original image and down-sampled depth
Image；
S3. calculate field depth and virtualization coefficient, using virtualization coefficient, virtualization process is carried out to down-sampled original image, blurred
Image；
S4. virtualization image is amplified to original image size, merges with original image, obtain weight focus image.
Described step S2 includes following sub-step：
S21. calculate the depth value of each of original image pixel according to principle of triangulation, obtain depth image；
S22. by down-sampled mode, original image is reduced, obtain down-sampled original image；
S23. using down-sampled mode, depth image is reduced, obtain down-sampled depth image.
Described step S3 includes following sub-step：
S31. photographic head parameter is combined according to down-sampled depth image and calculate field depth；
S32. each of down-sampled original image pixel is calculated with virtualization coefficient blurWeight：
blurWeight = (d-T1)/(T2–T1)；Wherein, d is the depth value of current pixel point, and T1, T2 are parameter preset, use
In the slope controlling formula；
S33. will virtualization coefficient be zero pixel as prospect, blur the pixel that coefficient is not zero as background, before realization
Scape is separated with background, and carries out virtualization process to each pixel as background, obtains blurring image.
Described step S4 includes following sub-step：
S41. virtualization image is amplified to original image size, obtains blurring enlarged drawing；
S42. according to virtualization coefficient, virtualization enlarged drawing and original image are merged：Virtualization enlarged drawing prompting blurs and clear
Scope and virtualization degree, be then coupled in original image, obtain weight focus image.
The invention has the beneficial effects as follows：Carry out blurring at calculating and the virtualization of coefficient for the image after down-sampled reducing
Reason, after obtaining blurring image, then is amplified to original image size and is merged with original image, greatly reduce process time；Especially
It is applied to mobile phone.
Brief description
Fig. 1 is the system principle diagram of the present invention；
Fig. 2 is method of the present invention flow chart.
Specific embodiment
Below in conjunction with the accompanying drawings technical scheme is described in further detail, but protection scope of the present invention is not limited to
Described below.
As shown in figure 1, a kind of system quickly generating large scale weight focus image, at image collection module and image
Reason module：
Image collection module, for obtaining original image；
Image processing module, including：
Image semantic classification submodule, calculates and down-sampled pretreatment for original image is carried out with depth information, obtains down-sampled
Original image and down-sampled depth image；
Image blurs submodule, for calculating field depth and virtualization coefficient, and using virtualization coefficient, down-sampled original image is entered
Row virtualization is processed, and obtains blurring image；
Image co-registration submodule, for virtualization image is amplified to original image size, is merged with original image, obtains focusing figure again
Picture.
A kind of described system quickly generating large scale weight focus image, also includes library module, for original image
Stored with weight focus image.
Described image collection module includes the different photographic head at least two visual angles, for gathering at least with different view
Two width original images.
In one embodiment, this system is mainly used in mobile phone（At least there is the mobile phone of two photographic head）, existing
The internal memory of mobile phone compares that PC ratio is relatively limited, and distributes to the then less of each APP, but the camera picture of main flow mobile phone on the market at present
Element is all very high, and picture size is very big, processes so big image than relatively time-consuming；The application is applied in mobile phone, Image Acquisition
Module is the camera of mobile phone（Or photographic head）, above-mentioned image processing module is the image procossing APP in mobile phone；Due to image
The virtualization of the virtualization calculating of coefficient and image be both for down-sampled after small-sized image（Down-sampled original image and down-sampled depth
Degree image）Carry out, amplify again after obtaining blurring image and merged with former large-size images, greatly reduce process time.
Described Image semantic classification submodule includes：
Depth information computing unit, for calculating the depth value of each of original image pixel according to principle of triangulation, obtains
To depth image；
Specifically, the photographic head of image collection module（At least two）Between there is a certain distance, so same scenery（Or
Pixel）There is certain difference by two camera lens imagings, both parallaxes, because the presence of parallax information, therefore according to triangle
Range measurement principle can calculate the scenery in original image（Or pixel）Depth value.
Depth image reaction is that in actual scene, object, with a distance from photographic head, can be seen that from mirror from depth map
The nearer local brightness of head is higher, and more remote local brightness is lower.
For example, definable original image storage information is（X, y, r, g, b), x, y represent the two-dimensional coordinate of current pixel point,
R, g, b represent redness respectively, green, the value of blue channel；Defining depth map storage information is（X, y, d), x, y represent current
The two-dimensional coordinate of pixel, d is the depth value of current pixel point.
Down-sampled unit, for being reduced for original image and depth image by down-sampled mode, obtains down-sampled
Original image and down-sampled depth image.
Described image virtualization submodule includes：
Field depth computing unit, calculates field depth for combining photographic head parameter according to down-sampled depth image；
Virtualization coefficient calculation unit, for calculating virtualization coefficient blurWeight to each of down-sampled original image pixel：
blurWeight = (d-T1)/(T2–T1)；Wherein, d is the depth value of current pixel point, and T1, T2 are parameter preset, use
In the slope controlling formula；
Virtualization processing unit, separates for image being carried out foreground and background according to virtualization coefficient：Virtualization coefficient is zero pixel
Point expression prospect, the pixel that virtualization coefficient is not zero represents background；And virtualization process is carried out to background according to virtualization coefficient, obtain
To virtualization image.
Described image co-registration submodule includes：
Virtualization image enlarging unit, for virtualization image is amplified to original image size, obtains blurring enlarged drawing；
Integrated unit, for by blurring enlarged drawing prompting virtualization and clearly scope and virtualization degree, being then coupled to former
In image, obtain weight focus image.
As shown in Fig. 2 a kind of method quickly generating large scale weight focus image, comprise the following steps：
S1. obtain original image using the image collection module at least with two photographic head；
S2. original image is carried out with depth information calculate and down-sampled pretreatment, obtain down-sampled original image and down-sampled depth
Image；
S3. calculate field depth and virtualization coefficient, using virtualization coefficient, virtualization process is carried out to down-sampled original image, blurred
Image；
S4. virtualization image is amplified to original image size, merges with original image, obtain weight focus image.
Described step S2 includes following sub-step：
S21. calculate the depth value of each of original image pixel according to principle of triangulation, obtain depth image；
S22. by down-sampled mode, original image is reduced, obtain down-sampled original image；
S23. using down-sampled mode, depth image is reduced, obtain down-sampled depth image.
In one embodiment, can process according to step S21 ~ 23 pair original image, obtain down-sampled original image and
Down-sampled depth image.
In another embodiment it is also possible to first original image is carried out down-sampled reduce process and obtain down-sampled original image,
Afterwards depth information is calculated with the pixel of down-sampled original image and obtain down-sampled depth image.
Described step S3 includes following sub-step：
S31. photographic head parameter is combined according to down-sampled depth image and calculate field depth；
S32. each of down-sampled original image pixel is calculated with virtualization coefficient blurWeight：
blurWeight = (d-T1)/(T2–T1)；Wherein, d is the depth value of current pixel point, and T1, T2 are parameter preset, use
In the slope controlling formula；
S33. will virtualization coefficient be zero pixel as prospect, blur the pixel that coefficient is not zero as background, before realization
Scape is separated with background, and carries out virtualization process to each pixel as background, obtains blurring image.
Described step S4 includes following sub-step：
S41. virtualization image is amplified to original image size, obtains blurring enlarged drawing；
S42. according to virtualization coefficient, virtualization enlarged drawing and original image are merged：Virtualization enlarged drawing prompting blurs and clear
Scope and virtualization degree, be then coupled in original image, obtain weight focus image.
Actual image is carried out in weight focus process, can first select the original image again focused, then to selected former
Image carries out Image semantic classification in step S2 ~ S4, image virtualization and image co-registration；First image capture module can also be obtained
All original images carry out pretreatment, then select the original image again focused, according to step S3 ~ S4 to this original image（Corresponding
Down-sampled original image and down-sampled depth image）Carry out image virtualization and image co-registration.
In this application, carry out blurring the calculating of coefficient for the image after down-sampled reducing and virtualization is processed, obtain void
After changing image, then it is amplified to original image size and is merged with original image, greatly reduce process time；It is particularly well-suited to handss
Machine.
Claims (10)
1. a kind of quickly generate large scale weight focus image system it is characterised in that：At image collection module and image
Reason module：
Image collection module, for obtaining original image；
Image processing module, including：
Image semantic classification submodule, calculates and down-sampled pretreatment for original image is carried out with depth information, obtains down-sampled
Original image and down-sampled depth image；
Image blurs submodule, for calculating field depth and virtualization coefficient, and using virtualization coefficient, down-sampled original image is entered
Row virtualization is processed, and obtains blurring image；
Image co-registration submodule, for virtualization image is amplified to original image size, is merged with original image, obtains focusing figure again
Picture.
2. according to claim 1 a kind of quickly generate large scale weight focus image system it is characterised in that：Also include
Library module, for storing to original image and weight focus image.
3. according to claim 1 a kind of quickly generate large scale weight focus image system it is characterised in that：Described
Image collection module includes at least two photographic head, for gathering at least two width original images with different view.
4. according to claim 1 a kind of quickly generate large scale weight focus image system it is characterised in that：Described
Image semantic classification submodule includes：
Depth information computing unit, for calculating the depth value of each of original image pixel according to principle of triangulation, obtains
To depth image；
Down-sampled unit, for reducing for original image and depth image by down-sampled mode, obtains down-sampled artwork
Picture and down-sampled depth image.
5. according to claim 1 a kind of quickly generate large scale weight focus image system it is characterised in that：Described
Image virtualization submodule includes：
Field depth computing unit, calculates field depth for combining photographic head parameter according to down-sampled depth image；
Virtualization coefficient calculation unit, for calculating virtualization coefficient blurWeight to each of down-sampled original image pixel：
blurWeight = (d-T1)/(T2–T1)；Wherein, d is the depth value of current pixel point, and T1, T2 are parameter preset, use
In the slope controlling formula；
Virtualization processing unit, separates for image being carried out foreground and background according to virtualization coefficient：Virtualization coefficient is zero pixel
Point expression prospect, the pixel that virtualization coefficient is not zero represents background；And virtualization process is carried out to background according to virtualization coefficient, obtain
To virtualization image.
6. according to claim 1 a kind of quickly generate large scale weight focus image system it is characterised in that：Described
Image co-registration submodule includes：
Virtualization image enlarging unit, for virtualization image is amplified to original image size, obtains blurring enlarged drawing；
Integrated unit, for by blurring enlarged drawing prompting virtualization and clearly scope and virtualization degree, being then coupled to former
In image, obtain weight focus image.
7. a kind of quickly generate large scale weight focus image method it is characterised in that：Comprise the following steps：
S1. obtain original image using the image collection module at least with two photographic head；
S2. original image is carried out with depth information calculate and down-sampled pretreatment, obtain down-sampled original image and down-sampled depth
Image；
S3. calculate field depth and virtualization coefficient, using virtualization coefficient, virtualization process is carried out to down-sampled original image, blurred
Image；
S4. virtualization image is amplified to original image size, merges with original image, obtain weight focus image.
8. according to claim 7 a kind of quickly generate large scale weight focus image method it is characterised in that：Described
Step S2 includes following sub-step：
S21. calculate the depth value of each of original image pixel according to principle of triangulation, obtain depth image；
S22. by down-sampled mode, original image is reduced, obtain down-sampled original image；
S23. using down-sampled mode, depth image is reduced, obtain down-sampled depth image.
9. according to claim 7 a kind of quickly generate large scale weight focus image method it is characterised in that：Described
Step S3 includes following sub-step：
S31. photographic head parameter is combined according to down-sampled depth image and calculate field depth；
S32. each of down-sampled original image pixel is calculated with virtualization coefficient blurWeight：
blurWeight = (d-T1)/(T2–T1)；Wherein, d is the depth value of current pixel point, and T1, T2 are parameter preset, use
In the slope controlling formula；
S33. will virtualization coefficient be zero pixel as prospect, blur the pixel that coefficient is not zero as background, before realization
Scape is separated with background, and carries out virtualization process to each pixel as background, obtains blurring image.
10. according to claim 7 a kind of quickly generate large scale weight focus image method it is characterised in that：Described
Step S4 include following sub-step：
S41. virtualization image is amplified to original image size, obtains blurring enlarged drawing；
S42. according to virtualization coefficient, virtualization enlarged drawing and original image are merged：Virtualization enlarged drawing prompting blurs and clear
Scope and virtualization degree, be then coupled in original image, obtain weight focus image.
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Application publication date: 20170215 |
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| RJ01 | Rejection of invention patent application after publication |