CN102842120A - Image blurring degree detection method based on supercomplex wavelet phase measurement - Google Patents

Image blurring degree detection method based on supercomplex wavelet phase measurement Download PDF

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CN102842120A
CN102842120A CN2012102995528A CN201210299552A CN102842120A CN 102842120 A CN102842120 A CN 102842120A CN 2012102995528 A CN2012102995528 A CN 2012102995528A CN 201210299552 A CN201210299552 A CN 201210299552A CN 102842120 A CN102842120 A CN 102842120A
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supercomplex
coefficient
wavelet
image
phase
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CN102842120B (en
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金晶
刘义鹏
沈毅
王强
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Harbin University of technology high tech Development Corporation
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Abstract

The invention discloses an image blurring degree detection method based on supercomplex wavelet phase measurement, and relates to an image resolution distinguishing method in image processing field. In order to overcome the fault of narrow detection range of an existing method, the image blurring degree detection method comprises following steps: first, transforming a noise image in a supercomplex wavelet mode to obtain a supercomplex wavelet phase coefficient; second, converting the supercomplex wavelet phase coefficient in a real part - imaginary part representation to a representation of amplitude - phase; third, counting the distribution character of a phase coefficient, and calculating distribution variance. The distribution variance is used to describe the distribution character of the supercomplex wavelet phase coefficient, so that texture variation between a clear image and a blurred image is effectively described, and the detection of blurring degree between different images can be achieved.

Description

Image blurring degree detecting method based on the measurement of supercomplex Wavelet Phase
Technical field
The present invention relates to a kind of image definition method of discrimination of image processing field, be specifically related to a kind of image blurring degree detecting method of measuring based on the supercomplex Wavelet Phase.
Background technology
Image definition/fuzzy the detection because its various vision is used is paid close attention to and receive widely, merges or the like such as the self-adaptation of the automatic focusing of digital micro-analysis imaging, remote Sensing Image Analysis, many focus charts picture.From a large amount of images that contains different fog-levels, select image automatically, can help the researcher to reduce very big workload for further graphical analysis with high definition.Because the different depth of field is difficult in the piece image each object is realized focusing on,, obtain the image of better visual effect so we can judge focal zone for many focus charts picture, and merge their clear area.These all need stable image blur operator.
Some article research image blur operators or similar notion have been arranged, measured like fuzzy detection, focusing etc.Most of operators comprise variance, image gradient, RMSE, PSNR etc., suppose that all the radio-frequency component of image has been decayed by point spread function.But these criterion functions have been ignored The noise, and noise can cause the variation of image radio-frequency component, but do not influence the integral image sharpness.The width that the wavelet coefficient of Tian J. and Chen J. discovery blurred picture distributes is narrower than the picture rich in detail, and these characteristics impel them to use the Laplacian mixture model with local auto-adaptive property that the marginal distribution of wavelet coefficient is carried out modeling.All above-mentioned operators of mentioning are all relevant with picture material itself, and for example, for the different images with same fog-level, the result that these operators calculate is different.
The supercomplex wavelet transformation (Hypercomplex Wavelet Transform, HWT), as a kind of new image analysis tool; Than wavelet transform; Have superior character, move constantly such as coefficient is approximate, phase coefficient provides more abundant image texture information etc.1999, B ü low made up for the first time the two-dimensional analysis signal in his PhD dissertation, and the concept extension of supercomplex Fourier transform to supercomplex Gabor conversion, is the predecessor of HWT, and uses it for difference estimation and Texture Segmentation.People such as Chan W. used in 2008 two-dimentional Hilbert transform (Hilbert Transform, HT) with the notion of analytic signal with the concept extension of dual-tree complex wavelet to onlapping number field, and be applied to difference estimation.HWT provides the subband of Quaternion Representation, and its coefficient can convert the form of an amplitude and three phase places through algebraic operation into---and its amplitude has to be similar to moves invariant feature, and three two-dimensional phases have comprised the geological information of describing local two-dimensional structure.
Represent ability because the HWT phase coefficient has texture, a kind of new approach is provided so statistics supercomplex Wavelet Phase coefficient is distributed as the detection of fog-level.
Summary of the invention
The objective of the invention is to overcome the existing narrower deficiency of method sensing range, provide a kind of based on supercomplex wavelet transformation (Hypercomplex Wavelet Transform, HWT) the image blur detection method of phase measurement.It can bring into play the ability that supercomplex Wavelet Phase texture is represented, sets up the corresponding relation between the distribution of different fog-levels and phase coefficient, thereby reaches the sensing range of widening image blurring degree.
The objective of the invention is to realize through following technical scheme:
Blurred picture is carried out HWT, convert the wavelet coefficient that obtains the representation of amplitude-phase place into, calculate the distribution statistics characteristic of phase coefficient, be used for the detected image blur level.
Process flow diagram of the present invention is as shown in Figure 1, and concrete steps are following:
Step 1: noise image is carried out HWT, obtain the supercomplex wavelet coefficient.This step is decomposed the multiresolution that image carries out higher-dimension, can excavate the inherent geometry of image, can effectively suppress noise, keeps the image border.
The supercomplex analytic signal is by signal itself
Figure 2012102995528100002DEST_PATH_IMAGE002
and its section H T(
Figure 2012102995528100002DEST_PATH_IMAGE004
;
Figure 2012102995528100002DEST_PATH_IMAGE006
) and complete HT(
Figure 2012102995528100002DEST_PATH_IMAGE008
) constitute
Figure 2012102995528100002DEST_PATH_IMAGE010
；
Wherein,
Figure 2012102995528100002DEST_PATH_IMAGE012
;
Figure 2012102995528100002DEST_PATH_IMAGE014
;
Figure 2012102995528100002DEST_PATH_IMAGE016
is hypercomplex three imaginary parts, and
Figure 2012102995528100002DEST_PATH_IMAGE018
is the structure result of supercomplex analytic signal.
Two dimension HT is equivalent to the HT that the row and column of matrix is done one dimension respectively.Can divide small echo for two dimension; The one dimension HT that
Figure 2012102995528100002DEST_PATH_IMAGE020
considers wavelet function is to
Figure 2012102995528100002DEST_PATH_IMAGE022
with yardstick equation
Figure 2012102995528100002DEST_PATH_IMAGE024
, and two-dimentional HWT can remember that work can divide the form of small echo product:
；
Figure 2012102995528100002DEST_PATH_IMAGE028
；
Figure 2012102995528100002DEST_PATH_IMAGE030
；
Figure 2012102995528100002DEST_PATH_IMAGE032
；
Through image and formula (2)-(5) are carried out convolution, just can obtain the HWT coefficient of noise image.Wherein, is the 2-d wavelet wave filter;
Figure 2012102995528100002DEST_PATH_IMAGE036
is supercomplex small echo low frequency coefficient;
Figure 2012102995528100002DEST_PATH_IMAGE038
is supercomplex small echo horizontal component coefficient;
Figure 2012102995528100002DEST_PATH_IMAGE040
is supercomplex small echo vertical component coefficient,
Figure 2012102995528100002DEST_PATH_IMAGE042
be supercomplex small echo diagonal components coefficient.
Step 2: the supercomplex wavelet coefficient that real part-imaginary part is represented converts amplitude-phase place representation into; Phase place can be expressed the texture information of image; When handling amplitude coefficient, the image texture information that receives the less influence of noise can be kept fully like this.
The supercomplex wavelet coefficient that is obtained by supercomplex small echo and image convolution can be expressed as:
Figure 2012102995528100002DEST_PATH_IMAGE044
；
It is the expression mode of real part-imaginary part; Wherein,
Figure 2012102995528100002DEST_PATH_IMAGE046
is the supercomplex algebraic symbol;
Figure 2012102995528100002DEST_PATH_IMAGE048
is supercomplex real part coefficient;
Figure 2012102995528100002DEST_PATH_IMAGE050
is supercomplex imaginary part i component coefficient;
Figure 2012102995528100002DEST_PATH_IMAGE052
is supercomplex imaginary part j component coefficient;
Figure 2012102995528100002DEST_PATH_IMAGE054
is supercomplex imaginary part k component coefficient;
Figure 2012102995528100002DEST_PATH_IMAGE056
,
Figure 2012102995528100002DEST_PATH_IMAGE058
is three imaginary parts.
According to the computation rule of supercomplex algebraically, formula (6) can convert following form into:
Figure 2012102995528100002DEST_PATH_IMAGE060
；
Wherein:
Figure 2012102995528100002DEST_PATH_IMAGE062
；
Figure 2012102995528100002DEST_PATH_IMAGE064
; is hypercomplex amplitude;
Figure 2012102995528100002DEST_PATH_IMAGE068
is hypercomplex phase place, and
Figure 2012102995528100002DEST_PATH_IMAGE070
is exponent sign.The calculation procedure at three phasing degree
Figure 2012102995528100002DEST_PATH_IMAGE068A
is following:
1) at first with supercomplex unit's of being normalized to supercomplex, i.e.
Figure 2012102995528100002DEST_PATH_IMAGE072
.
2) Calculation
Figure 2012102995528100002DEST_PATH_IMAGE074
:
Figure 2012102995528100002DEST_PATH_IMAGE076
.
3) calculate and
Figure 2012102995528100002DEST_PATH_IMAGE080
: if ,
Figure 2012102995528100002DEST_PATH_IMAGE084
so; Otherwise
Figure 2012102995528100002DEST_PATH_IMAGE086
be
Figure 2012102995528100002DEST_PATH_IMAGE088
perhaps;
Wherein,
Figure 2012102995528100002DEST_PATH_IMAGE090
;
Figure 2012102995528100002DEST_PATH_IMAGE092
,
Figure 2012102995528100002DEST_PATH_IMAGE094
.
4) if
Figure 2012102995528100002DEST_PATH_IMAGE096
; Need adjustment : if
Figure 2012102995528100002DEST_PATH_IMAGE098
,
Figure 2012102995528100002DEST_PATH_IMAGE100
; if
Figure 2012102995528100002DEST_PATH_IMAGE102
,
Figure 2012102995528100002DEST_PATH_IMAGE104
.
Step 3: the distribution of statistics phase coefficient, calculate distribution variance.
The distribution of statistics phase coefficient; In its minimum value and peaked interval, is minizone with 0.01 index variation with supercomplex Wavelet Phase coefficient, adds up the number of each minizone supercomplex wavelet coefficient; The total number of image pixel is known, and then the probability that obtains distributing.
Fig. 2-shown in Figure 9 is phase coefficient distribution histogram; Image behind
Figure DEST_PATH_IMAGE106A
process 7*7 window Gaussian Blur is narrower than the picture rich in detail; Because the pixel intensity of blurred picture presents consistance, and the high frequency of texture information has been weakened.Therefore, need an operator to describe the distribution histogram shape directly related with image definition.Variance is being controlled the width of Gaussian distribution, and the present invention utilizes the notion of variance to describe the form parameter of image blurring degree.The image blur operator definitions is:
Figure DEST_PATH_IMAGE108
；
Wherein, representes high frequency;
Figure DEST_PATH_IMAGE112
is the variance of QWT coefficient, and subscript is correspondingly represented the level and the vertical component of phase place.The size of this operator is used for describing the fog-level of image, is worth more for a short time, and representative image is fuzzy more.The computing formula of variance
Figure DEST_PATH_IMAGE112A
is:
Figure DEST_PATH_IMAGE116
(10);
Wherein,
Figure DEST_PATH_IMAGE118
is supercomplex Wavelet Phase coefficient;
Figure DEST_PATH_IMAGE120
is supercomplex wavelet coefficient phase place average;
Figure DEST_PATH_IMAGE122
is the number of supercomplex Wavelet Phase coefficient, following table
Figure DEST_PATH_IMAGE012A
mark coefficient index.
The present invention utilizes variance to describe the distribution characteristics of supercomplex Wavelet Phase coefficient, has described the texture variations between clear and the blurred picture effectively, can realize the detection of fog-level between the different images.Compared with prior art have following advantage:
1) image blur detection method proposed by the invention is utilized the supercomplex wavelet transformation, and compares based on method of wavelet, has measurement stability, wideer sensing range.
2) the present invention introduces the supercomplex wavelet transformation and carries out image denoising; In the supercomplex wavelet field; The present invention proposes phase place high-frequency information measuring method, with data by MoM and MEI, has lower complexity and measures consistance; Blur level result of calculation and image itself is irrelevant, has approximate result of calculation for the image of the identical fog-level of different images.
Description of drawings
Fig. 1 is the image blur detection method process flow diagram of measuring based on the supercomplex Wavelet Phase;
Fig. 2 is the supercomplex small echo low frequency coefficient of the picture rich in detail of phase place
Figure DEST_PATH_IMAGE124
expression;
Fig. 3 is the supercomplex small echo horizontal component coefficient of the picture rich in detail of phase place expression;
Fig. 4 is the supercomplex small echo vertical component coefficient of the picture rich in detail of phase place
Figure DEST_PATH_IMAGE124AA
expression;
Fig. 5 is the supercomplex small echo diagonal components coefficient of the picture rich in detail of phase place
Figure DEST_PATH_IMAGE124AAA
expression;
Fig. 6 is the supercomplex small echo low frequency coefficient of the image after the bluring of phase place
Figure DEST_PATH_IMAGE124AAAA
expression;
Fig. 7 is the supercomplex small echo horizontal component coefficient of the image after the bluring of phase place
Figure DEST_PATH_IMAGE124AAAAA
expression;
Fig. 8 is the supercomplex small echo vertical component coefficient of the image after the bluring of phase place
Figure DEST_PATH_IMAGE124AAAAAA
expression;
Fig. 9 is the supercomplex small echo diagonal components coefficient of the image after the bluring of phase place
Figure DEST_PATH_IMAGE124AAAAAAA
expression;
Figure 10 is a picture rich in detail;
Figure 11 is the image of Figure 10 after fuzzy;
Figure 12 is that Figure 10 is through the result of calculation after bluring in various degree;
Figure 13 is a test pattern one;
Figure 14 is a test pattern two;
Figure 15 is a test pattern three;
Figure 16 is a test pattern four;
Figure 17 is a test pattern five;
Figure 18 is a test pattern six;
Figure 19 is a test pattern seven;
Figure 20 is a test pattern eight;
Figure 21 is a test pattern nine;
Figure 22 is a test pattern ten;
Figure 23 is the result of calculation of the different fog-levels of ten width of cloth test patterns.
Embodiment
Set forth embodiment of the present invention through standard testing image simulation example below:
Execution in step one: the image to picture rich in detail and its after fuzzy carries out HWT, obtains the supercomplex wavelet coefficient that real part-imaginary part is represented.
With Figure 10-11 is example, and image resolution ratio is 256*256, and blurred picture is the result who picture rich in detail is done Gauss's mask convolution.
Execution in step two: the supercomplex wavelet coefficient image transitions that real part-imaginary part that step 1 is obtained is represented is amplitude-phase place representation.
The phase component that is applied to the blur level detection that Figure 10-11 is corresponding is seen Fig. 2-9.
Execution in step three: the distribution character of statistics phase coefficient, calculate distribution variance.
Because the phase component that supercomplex wavelet amplitude-phase place is represented has the ability that texture is represented, the texture of blurred picture can be affected, so the result of calculation behind two width of cloth image normalizations shown in Figure 10 of the present invention-11 is respectively 1 and 0.5335.Figure 12 is the result of calculation of the image after bluring through the template of different sizes; Horizontal ordinate is a template size, and ordinate is a variance result of calculation, can find out the increase along with fog-level; Result of calculation diminishes, and method of the present invention can active zone be told the image of different fog-levels.
Figure 13-22 is 10 width of cloth test patterns, measures, can obtain the result of calculation of Figure 23 through utilizing method of the present invention to carry out blur level behind same degree fuzzy.Be not more than in 9 in template size, have similar result of calculation between the different images for same fog-level; Between the different fog-level different images, also can pass through the different fog-level of result of calculation differentiate between images.

Claims (4)

1. the image blurring degree detecting method of measuring based on the supercomplex Wavelet Phase is characterized in that said method comprises the steps:
Step 1: noise image is carried out HWT, obtain the supercomplex wavelet coefficient:
Figure 2012102995528100001DEST_PATH_IMAGE002
;
Wherein:
Figure 2012102995528100001DEST_PATH_IMAGE004
is the supercomplex algebraic symbol;
Figure 2012102995528100001DEST_PATH_IMAGE006
is supercomplex real part coefficient;
Figure 2012102995528100001DEST_PATH_IMAGE008
is supercomplex imaginary part i component coefficient; is supercomplex imaginary part j component coefficient;
Figure 2012102995528100001DEST_PATH_IMAGE012
is supercomplex imaginary part k component coefficient;
Figure 2012102995528100001DEST_PATH_IMAGE014
,
Figure 2012102995528100001DEST_PATH_IMAGE016
is three imaginary parts;
Step 2: the supercomplex wavelet coefficient that real part-imaginary part is represented converts amplitude-phase place representation into:
Figure 2012102995528100001DEST_PATH_IMAGE018
;
Wherein:
Figure 2012102995528100001DEST_PATH_IMAGE020
; ;
Figure 2012102995528100001DEST_PATH_IMAGE024
is hypercomplex amplitude;
Figure 2012102995528100001DEST_PATH_IMAGE026
is hypercomplex phase place, and
Figure 2012102995528100001DEST_PATH_IMAGE028
is exponent sign;
Step 3: the distribution of statistics phase coefficient, calculate distribution variance, utilize variance to describe the form parameter of image blurring degree.
2. the image blurring degree detecting method of measuring based on the supercomplex Wavelet Phase according to claim 1; It is characterized in that in the said step 2 that the calculation procedure of phasing degree is following:
1) at first with supercomplex
Figure DEST_PATH_IMAGE004A
unit's of being normalized to supercomplex, i.e.
Figure 2012102995528100001DEST_PATH_IMAGE030
;
2) Calculation
Figure 2012102995528100001DEST_PATH_IMAGE032
:
Figure 2012102995528100001DEST_PATH_IMAGE034
;
3) calculate
Figure 2012102995528100001DEST_PATH_IMAGE036
and
Figure 2012102995528100001DEST_PATH_IMAGE038
: if ,
Figure 2012102995528100001DEST_PATH_IMAGE042
so; Otherwise
Figure 2012102995528100001DEST_PATH_IMAGE044
be
Figure 2012102995528100001DEST_PATH_IMAGE046
perhaps;
Wherein,
Figure 2012102995528100001DEST_PATH_IMAGE048
;
Figure 2012102995528100001DEST_PATH_IMAGE050
,
Figure 2012102995528100001DEST_PATH_IMAGE052
;
4) if
Figure 2012102995528100001DEST_PATH_IMAGE054
; Need adjustment
Figure DEST_PATH_IMAGE036A
: if
Figure 2012102995528100001DEST_PATH_IMAGE056
,
Figure 2012102995528100001DEST_PATH_IMAGE058
; if
Figure 2012102995528100001DEST_PATH_IMAGE060
,
Figure 2012102995528100001DEST_PATH_IMAGE062
.
3. the image blurring degree detecting method of measuring based on the supercomplex Wavelet Phase according to claim 1; It is characterized in that in the said step 3; The image blur operator definitions is: ; Be worth more for a short time, representative image is fuzzy more;
Wherein, representes high frequency; is the variance of QWT coefficient, and subscript
Figure 2012102995528100001DEST_PATH_IMAGE070
is correspondingly represented the level and the vertical component index of phase place.
4. the image blurring degree detecting method of measuring based on the supercomplex Wavelet Phase according to claim 1; It is characterized in that in the said step 3 that the computing formula of variance is:
Figure 2012102995528100001DEST_PATH_IMAGE072
;
Wherein,
Figure 2012102995528100001DEST_PATH_IMAGE074
is supercomplex Wavelet Phase coefficient; is supercomplex wavelet coefficient phase place average;
Figure 2012102995528100001DEST_PATH_IMAGE078
is the number of supercomplex Wavelet Phase coefficient, following table
Figure 2012102995528100001DEST_PATH_IMAGE080
mark coefficient index.
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CN106504221A (en) * 2016-10-13 2017-03-15 江南大学 Based on the Medical image fusion new method that quaternion wavelet converts context mechanism
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CN116847209A (en) * 2023-08-29 2023-10-03 中国测绘科学研究院 Log-Gabor and wavelet-based light field full-focusing image generation method and system
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