CN102521798A - Image automatic recovering method for cutting and selecting mask structure based on effective characteristic - Google Patents
Image automatic recovering method for cutting and selecting mask structure based on effective characteristic Download PDFInfo
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Abstract
The invention relates to an image automatic recovering method for cutting and selecting a mask structure based on an effective characteristic. The image automatic recovering method is characterized by comprising the following steps of: automatically recovering an image without priori information; automatically recovering movement blur under a complex movement trace and out-of-focus blur within a certain range by using an algorithm under the condition of no blur form or blur kernel parameter; increasing a processing speed and introducing a pre-enhancement step into an iteration process to reduce iteration times; performing calculation by using an image gradient instead of an image pixel value; obtaining a more clear processed image; and carrying out pre-enhancement on the strong edge region of the image before recovering. The method has the advantage that the algorithm is more stable.
Description
Technical field：
The present invention relates to a kind of Digital Image Processing, and in particular to the image automatic recovery method for selecting mask construction is cut out based on validity feature.
Background technology
Assuming that blurred picture is to do convolution by picture rich in detail and fuzzy core to obtain, general image-recovery technique（Non-blind is recovered）It is that fuzzy form is speculated by experience by observing blurred picture,（Such as motion blur, out of focus fuzzy）, and then the parameter about fuzzy core is tested out, finally carrying out deconvolution to blurred picture obtains picture rich in detail.
Used in the same trade it is mostly be non-blind recover：
The major defect that non-blind is recovered is to lack the degree of accuracy by empirical fuzzy core, and particularly with the motion blur that track is more complicated, this mode can not accurately estimate fuzzy core, causes non-blind to recover narrow application range.
Blind image restoration does not need the prior information about fuzzy core, it is only necessary to which a width blurred picture can be carried out the recovery of the complicated process hypograph that degrades, and the scope of application is wider, and recovery effects are also more preferable, more accurate.
There is a kind of simple blind recovery to need fuzzy form, it is known that calculating the parameter of fuzzy core and doing the image after deconvolution is restored.The shortcoming of this method recovers similar, it is necessary to pre-estimate fuzzy form with non-blind, and the shape or track requirements of fuzzy core are fairly simple.
Existing patent is simple blind recovery mostly, still there is to fuzzy form it is assumed that such as：The blind restoration method of motion blur image（Application number：200810115097.5, applicant：BJ University of Aeronautics ＆ Astronautics）, recovered by the size and Orientation for estimating motion blur core, and movement locus complexity is various under actual conditions, is substantially not present a single direction, applicability is not wide in this approach for institute.
Blind recovery technology shortcoming in existing literature：The big processing speed of operand is slow, inadequate robust (image recovered can deviate artwork), high to parameter request（Author：Jiajiaya, article：High-quality Motion Deblurring from a Single Image）, excessively strengthen, constrain fuzzy core the excess smoothness or excessive sparse for excessively causing fuzzy core（Author：Cho, article：Fast Motion Deblurring）.
The content of the invention
In order to overcome drawbacks described above, the image automatic recovery method of mask construction is selected it is an object of the invention to provide a kind of cut out based on validity feature.
To achieve these goals, the present invention is adopted the following technical scheme that：
The image automatic recovery method for selecting mask construction is cut out based on validity feature, its step is：
1. the automatic recovery of image, it is not necessary to prior information
This algorithm can be in the case of no fuzzy form and fuzzy nuclear parameter, the automatic motion blur recovered under Comlex-locus and out of focus fuzzy in the range of a certain size
2. improve processing speed
Pre- enhancing step is introduced in an iterative process, reduces iterations；
Calculated using image gradient rather than image pixel value.
3. obtain image after apparent processing
Image strong edge region is strengthened in advance before recovering.
4. algorithm is more stable
Sanction choosing is carried out to gradient image and obtains mask, the region that mask is zero is not involved in computing, it is achieved thereby that the screening to validity feature.Using the framework of multiple dimensioned processing, each yardstick is handled successively using the method for alternating iteration, fuzzy core to be estimated is added（Point spread function PSF）The stability of algorithm when larger；
The pre- enhancing step of picture rich in detail is added, the strong edge region of image is strengthened in advance using guiding filtering, the tonal range all the time using former blurred picture during enhanced is used as guidance, it is to avoid excessive enhancing；
Sanction choosing is carried out to gradient image:Recovery feature is evaluated in terms of the density of each position and direction two from gradient image, sets and cuts out choosing rule, obtain pattern mask, the region that mask value is zero will not participate in computing, effectively reduce the noise of result fuzzy core；
The estimation of fuzzy core is carried out in gradient image, and process is divided into coarse estimation and fine two kinds of estimation：First time iteration carries out coarse estimation, obtains the coverage of fuzzy core, i.e., the gradation zone of structure periphery to be restored is that the fine sanction of next step iterative gradient image selects offer condition；The iterative process carried out afterwards is fine estimation, and now image is selected by correct cut out so that the estimation procedure of fuzzy core more robust.
Quick image non-blind recovery algorithms are devised, because adding for several steps make it that our algorithm robustness is higher before, slightly worse non-blind restoration result can be tolerated so that algorithm can further improve speed.
The blind recovery algorithms flow of single scale（Flow chart right half part）It is summarized as follows
(1) blurred picture is read in first
, manual setting fuzzy core size (numerical value need to only be more than realistic blur core size).
The process of the blind recovery of intermediate iteration is carried out, including pre- enhancing image, acquisition mask, sanction select gradient image, fuzzy kernel estimates and a non-blind recovering step, the picture rich in detail estimated
And fuzzy core。
(2) iteration, obtains final restoration result.
Multiple dimensioned blind recovering step is as follows：
(1) first on most thick yardstick 2 (by taking three yardsticks as an example), the estimation picture rich in detail that the blind recovery processing of single scale obtains the yardstick is carried out
With
。
(2) will
Up-sampling obtains the initial value of the fuzzy core on yardstick 1
, the blind recovery processing of single scale is carried out, the estimation picture rich in detail of yardstick 1 is obtainedWith
。
(3) (2) step is circulated, until obtaining estimating picture rich in detail on yardstick 0
With
Untill.
Last point of existing overview flow chart, now explains the principle and implementation process of critical workflow：
Image strengthens in advance：
The strong edge region of image is strengthened in advance using guiding filtering, the tonal range all the time using former blurred picture during enhanced is used as guidance, it is to avoid excessively enhancing；
Sanction selects gradient image：
It is to select to recover image most helpful feature participation computing that sanction, which selects purpose, and the standard of characteristic quantification is to cut out choosing rule mainly to have two：
1. the gradient distribution density statistics of each pixel peripheral region, it is to avoid using the excessive intensive region of edge distribution, the Density Distribution and threshold value at edge can be estimated by the coverage of original gradient image and fuzzy core；
Pixel number in 2 gradient images on each gradient direction should keep balance, be realized by the scanning that (0,45,90,135 degree) four gradient directions are carried out to image.
Comprehensive two kinds of target setting threshold values, obtain sanction and select mask, carry out the sanction choosing of image.
Assuming that original image is I, mask is M, then cuts out image I ' after choosing and be：, 
Representing matrix dot product
Gradient image cuts out the post processing after choosing is finished：If selected region only accounts for the sub-fraction of artwork size, artwork is further cut out, gradient map is also done cuts out accordingly, reduce the image size for participating in computing.
（It is coarse/fine）Fuzzy kernel estimates：
Fuzzy kernel estimates are added without regular terms, to prevent the excess smoothness or sparse of estimated result.
Rough Fuzzy kernel estimates, using view picture blurred picture
With the gradient image of pre- enhancing image
Ambiguous estimation core
That is, minimize
Wherein,
Kernel estimates are finely obscured, using the blurred picture gradient cut out after choosing
Strengthen and cut out the gradient image selected in advance
Ambiguous estimation core
Exactly minimize
(2)
Wherein,
(
) represent opsition dependent dot product；
Expansion is：
Non-blind is recovered：
Utilize the fuzzy core of estimation
And blurred pictureAdd L2 regular terms and carry out non-blind recovery, that is, minimize
The present invention adds the pre- enhancing step of picture rich in detail, the strong edge region of image is strengthened in advance using guiding filtering, the tonal range all the time using former blurred picture during enhanced is used as guidance, it is to avoid excessive enhancing；Sanction choosing is carried out to gradient image:Recovery feature is evaluated in terms of the density of each position and direction two from gradient image, sets and cuts out choosing rule, obtain pattern mask, the region that mask value is zero will not participate in computing, effectively reduce the noise of result fuzzy core；The estimation of fuzzy core is carried out in gradient image, and process is divided into coarse estimation and fine two kinds of estimation：First time iteration carries out coarse estimation, obtains the coverage of fuzzy core, i.e., the gradation zone of structure periphery to be restored is that the fine sanction of next step iterative gradient image selects offer condition；The iterative process carried out afterwards is fine estimation, and now image is selected by correct cut out so that the estimation procedure of fuzzy core more robust.
Brief description of the drawings
Fig. 1 is schematic diagram of the invention.
Embodiment
Further illustrate below in conjunction with the accompanying drawings：
As shown in figure 1, a kind of this method realization is sequentially：（Only illustrate overall procedure herein, specific each link book " accompanying drawing and its brief description " of telling somebody what one's real intentions are has been described）
Blurred picture I is inputted, fuzzy core size psfSZ is preset, it is assumed that the down-sampled number of times of highest is set to H, multiple dimensioned processing is performed
1. by I it is down-sampled be lowest scale, be designated as yardstick H, at the same by psfSZ press down-sampled scale smaller,
A) the blind recovery processing of single scale is performed, i.e. order is performed：(i) enhancing image, (ii) acquisition mask, (iii) sanction select gradient image, (iv) to obscure kernel estimates in advance and (v) non-blind is recovered to walk, the picture rich in detail estimated
And fuzzy core
B) iteration, will
With
(i-v) is until end condition in being repeated a) as input
2. I is down-sampled for time low yardstick, yardstick H-1 is designated as, while psfSZ is pressed into down-sampled scale smaller, willUp-sampling obtains obtaining the initial value of fuzzy core on yardstick H-1,
A) the blind recovery processing of single scale is carried out, order is performed：(i) enhancing image, (ii) acquisition mask, (iii) sanction select the fuzzy kernel estimates of gradient image, (iv) and (v) non-blind to recover to walk in advance, obtain yardstick H-1 estimation picture rich in detail
And fuzzy core。
B) iteration, will
With
(i-v) is until end condition in being repeated a) as input
3. repeating step 2, until reaching yardstick 0, obtain estimating picture rich in detail
And fuzzy core
4. output estimation picture rich in detail
And fuzzy core
。
Claims (2)
1. cut out based on validity feature and select the image automatic recovery method of mask construction, it is characterised in that its step is：
The automatic recovery of image, it is not necessary to prior information；
This algorithm can be in the case of no fuzzy form and fuzzy nuclear parameter, the automatic motion blur recovered under Comlex-locus and out of focus fuzzy in the range of a certain size；
Improve processing speed
Pre- enhancing step is introduced in an iterative process, reduces iterations；
Calculated using image gradient rather than image pixel value；
Obtain image after apparent processing；
Image strong edge region is strengthened in advance before recovering；
Algorithm is more stable；
Quick image non-blind recovery algorithms are devised, due to the blind recovery algorithms of addition single scale of several steps before, slightly worse non-blind restoration result can be tolerated so that algorithm can further improve speed.
2. as claimed in claim 1 cut out the image automatic recovery method for selecting mask construction based on validity feature, it is characterised in that：The blind recovery algorithms flow of single scale（Flow chart right half part）It is summarized as follows
Blurred picture is read in first
, manual setting fuzzy core size (numerical value need to only be more than realistic blur core size),
The process of the blind recovery of intermediate iteration is carried out, including pre- enhancing image, acquisition mask, sanction select gradient image, fuzzy kernel estimates and a non-blind recovering step, the picture rich in detail estimated
And fuzzy core
,
Iteration, obtains final restoration result,
Multiple dimensioned blind recovering step is as follows：
(1) first on most thick yardstick 2 (by taking three yardsticks as an example), the estimation picture rich in detail that the blind recovery processing of single scale obtains the yardstick is carried out
With
,
(2) will
Up-sampling obtains the initial value of the fuzzy core on yardstick 1
, the blind recovery processing of single scale is carried out, the estimation picture rich in detail of yardstick 1 is obtained
With
,
(3) (2) step is circulated, until obtaining estimating picture rich in detail on yardstick 0With
Untill,
Last point of existing overview flow chart, now explains the principle and implementation process of critical workflow：
Image strengthens in advance：
The strong edge region of image is strengthened in advance using guiding filtering, the tonal range all the time using former blurred picture during enhanced is used as guidance, it is to avoid excessively enhancing；
Sanction selects gradient image：
It is to select to recover image most helpful feature participation computing that sanction, which selects purpose, and the standard of characteristic quantification is to cut out choosing rule mainly to have two：
1. the gradient distribution density statistics of each pixel peripheral region, it is to avoid using the excessive intensive region of edge distribution, the Density Distribution and threshold value at edge can be estimated by the coverage of original gradient image and fuzzy core；
Pixel number in 2 gradient images on each gradient direction should keep balance, be realized by the scanning that (0,45,90,135 degree) four gradient directions are carried out to image,
Comprehensive two kinds of target setting threshold values, obtain sanction and select mask, carry out the sanction choosing of image,
Assuming that original image is I, mask is M, then cuts out image I ' after choosing and be：
, 
Representing matrix dot product
Gradient image cuts out the post processing after choosing is finished：If selected region only accounts for the sub-fraction of artwork size, artwork is further cut out, gradient map is also done cuts out accordingly, reduces the image size for participating in computing,
（It is coarse/fine）Fuzzy kernel estimates：
Fuzzy kernel estimates are added without regular terms, to prevent the excess smoothness or sparse of estimated result,
Rough Fuzzy kernel estimates, using view picture blurred picture
With the gradient image of pre- enhancing image
Ambiguous estimation core
That is, minimize
Wherein,
Kernel estimates are finely obscured, using the blurred picture gradient cut out after choosing
Strengthen and cut out the gradient image selected in advance
Ambiguous estimation core
Exactly minimize
(2)
Wherein,
(
) represent opsition dependent dot product；
Expansion is：
Non-blind is recovered：
Utilize the fuzzy core of estimation
And blurred picture
Add L2 regular terms and carry out non-blind recovery, that is, minimize
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Cited By (3)
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| CN106875349A (en) * | 2016-12-30 | 2017-06-20 | 无锡高新兴智能交通技术有限公司 | The computational methods and blind image restoring method of fuzzy core in blind image restoring method |
| CN109345474A (en) * | 2018-05-22 | 2019-02-15 | 南京信息工程大学 | Image motion based on gradient field and deep learning obscures blind minimizing technology |
| CN112862715A (en) * | 2021-02-08 | 2021-05-28 | 天津大学 | Real-time and controllable scale space filtering method |
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| CN101359398A (en) * | 2008-06-16 | 2009-02-04 | 北京航空航天大学 | Blind restoration method for moving blurred image |
| US20100080487A1 (en) * | 2006-10-23 | 2010-04-01 | Yitzhak Yitzhaky | Blind restoration of images degraded by isotropic blur |
| CN101930601A (en) * | 2010-09-01 | 2010-12-29 | 浙江大学 | Edge information-based multi-scale blurred image blind restoration method |
| CN101986345A (en) * | 2010-11-04 | 2011-03-16 | 浙江大学 | Image deblurring method |
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Patent Citations (5)
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| US20100080487A1 (en) * | 2006-10-23 | 2010-04-01 | Yitzhak Yitzhaky | Blind restoration of images degraded by isotropic blur |
| US20080240607A1 (en) * | 2007-02-28 | 2008-10-02 | Microsoft Corporation | Image Deblurring with Blurred/Noisy Image Pairs |
| CN101359398A (en) * | 2008-06-16 | 2009-02-04 | 北京航空航天大学 | Blind restoration method for moving blurred image |
| CN101930601A (en) * | 2010-09-01 | 2010-12-29 | 浙江大学 | Edge information-based multi-scale blurred image blind restoration method |
| CN101986345A (en) * | 2010-11-04 | 2011-03-16 | 浙江大学 | Image deblurring method |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106875349A (en) * | 2016-12-30 | 2017-06-20 | 无锡高新兴智能交通技术有限公司 | The computational methods and blind image restoring method of fuzzy core in blind image restoring method |
| CN109345474A (en) * | 2018-05-22 | 2019-02-15 | 南京信息工程大学 | Image motion based on gradient field and deep learning obscures blind minimizing technology |
| CN112862715A (en) * | 2021-02-08 | 2021-05-28 | 天津大学 | Real-time and controllable scale space filtering method |
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Denomination of invention: Automatic image restoration method based on effective feature cut mask construction Effective date of registration: 20200921 Granted publication date: 20150715 Pledgee: Hangzhou Yuhang Financial Holding Co.,Ltd. Pledgor: ZHEJIANG ICARE VISION TECHNOLOGY Co.,Ltd. Registration number: Y2020330000737 |