CN107168930A  A kind of tight frame Grouplet associated domain computational methods  Google Patents
A kind of tight frame Grouplet associated domain computational methods Download PDFInfo
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 CN107168930A CN107168930A CN201710356482.8A CN201710356482A CN107168930A CN 107168930 A CN107168930 A CN 107168930A CN 201710356482 A CN201710356482 A CN 201710356482A CN 107168930 A CN107168930 A CN 107168930A
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 230000003252 repetitive Effects 0.000 claims description 5
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 G—PHYSICS
 G06—COMPUTING; CALCULATING; COUNTING
 G06F—ELECTRIC DIGITAL DATA PROCESSING
 G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
 G06F17/10—Complex mathematical operations
 G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, KarhunenLoeve, transforms

 G—PHYSICS
 G06—COMPUTING; CALCULATING; COUNTING
 G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
 G06T7/00—Image analysis
 G06T7/40—Analysis of texture

 G—PHYSICS
 G06—COMPUTING; CALCULATING; COUNTING
 G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
 G06T2207/00—Indexing scheme for image analysis or image enhancement
 G06T2207/20—Special algorithmic details
 G06T2207/20048—Transform domain processing
Abstract
The embodiment of the invention discloses a kind of computational methods of tight frame Grouplet associated domains, by the way that line detective operators are incorporated into the calculating of associated domain, on the premise of unobvious loss coefficient matrix degree of rarefication and energy perfect reconstruction original image, significantly reduce the time loss of conventional tight frame Grouplet conversion, improve the efficiency of conversion, application of the tight frame Grouplet conversion in image procossing direction is widened, with important practical significance.
Description
Technical field
The present invention relates to a kind of image processing method, more particularly to a kind of tight frame Grouplet associated domain computational methods.
Background technology
Strokes lines can just sketch the contours of the apparent figure or texture of a feature.Natural image typically all include by
The region that local direction structure is constituted.Complex geometry information in image can preferably be represented and the rarefaction representation of image is obtained be
The key of image procossing.
The resolution ratio that wavelet transformation can come in adaptive adjustment image procossing according to the systematicness of topography, thus
It is especially efficient for the expression of image.But wavelet basis is not optimal on the image for representing geometry, because it
Side's support can not adaptively represent geometry of direction attribute.Curvelets, Contourlets, Curvelets, Bnadlets,
Wedgelets is successively suggested to improve this problem, and achieves good effect in the application of specific image procossing
Really, however their improvement effects expected in theory in the application of real image processing are so good, may be due to
The texture structure of these pictures excessively complexity is so that their base can not represent image well.In order to overcome this shortcoming,
Mallat proposed Grouplet conversion in 2008.This is a kind of brandnew conversion, and its base can be as image be in different chis
Spend the change of lower geometry and change, thus the geometric properties of image can be utilized to greatest extent.Meanwhile, Grouplet becomes
The calculating changed is simple, and the transform method of itself is exactly a kind of quick calculation method.
Tight frame Grouplet decomposes the calculating comprising associated domain layer and two layers of coefficient layer.Tight frame Grouplet is converted
The searching of middle associated domain has very big influence to the performance of conversion.Tight frame Grouplet conversion employs Block
Matching algorithms find associated domain, and the benefit of this method is that it is discretization, can accurately reflect each pixel
Change, but be due to need to delimit grid in advance, thus can not be adaptively according to picture structure selected directions, thus can not
The image for including complex texture is represented well.
The content of the invention
Technical problem to be solved of the embodiment of the present invention is that there is provided a kind of tight frame Grouplet associated domains calculating side
Method.The high complexity that Block Matching algorithms are brought in former conversion can be reduced, tight frame Grouplet conversion is improved
Efficiency.
In order to solve the abovementioned technical problem, the embodiments of the invention provide a kind of tight frame Grouplet associated domains calculating side
Method, comprises the following steps：
1) grayscale map is converted into the image of input；
2) the grayscale map Grouplet is decomposed, calculates jth layer coefficients layer coefficients, calculate optimal by line detective operators
Match point simultaneously calculates jth layer associated domain layer coefficients, wherein 1≤j≤J；
3) repetitive cycling step 2), finished until J layer coefficients are all calculated, wherein J is given empirical value.
Further, 3 × 3 matrix that the line detective operators template is used.
Further, the formula of the line detective operators calculating Optimum Matching point is：
WhereinBe withCentered on 3 × 3 image data matrixs after binaryzation；OP_{i}[j] is detection direction i
Line detective operators, i represents to gather a direction in {+45 °, 0 °, 45 ° }；Symbol j represents jth of position of homography.
Further, the step of line detective operators calculating Optimum Matching point is：
1) in jth layer, with pointCentered on, it is 3 × 3 to choose size, the data matrix BP that pixel value is constituted,
2) data matrix BP described in binaryzation_{3×3}, note matrix B P_{3×3}Average be Av, then work as BP_{x,y}Less than for the average
During Av, BP_{x,y}Value 0, is otherwise 1,
3) formula for calculating Optimum Matching point using the line detective operators is tried to achieve a littleOptimum Matching point m,
4) associated domain layer coefficients are calculatedWith coefficient layer coefficients { d_{j}[m],a_{J}[m]}_{1≤j≤J}, wherein
Associated domain layer coefficients：
When j increases from 1 to J, coefficient layer factor v is updated by below equation：
Wherein, a [m] represents the equal value coefficient at point m, as j=1, and a [m] is the pixel value at point m；d_{j}[m] represents point
Difference coefficient at m；S [m] represents the size of support frame at point m, as j=1, s [m]=1.As j=J, a_{J}[m] is by public affairs
FormulaCalculating is obtained
5) repetitive cycling abovementioned steps, to 1 to J layer of institute, a little all matching is completed.
Implement the embodiment of the present invention, have the advantages that：The method of the present invention reduces Block Matching calculations
The high complexity that method is brought, when being converted applied to tight frame Grouplet, will greatly save the time of conversion, improves efficiency.
Brief description of the drawings
Fig. 1 is the flowage structure schematic diagram of the present invention；
Fig. 2 is for 45 °, 0 ° ,+45 ° of detection templates；
Fig. 3 is the correction data of line detection algorithms and Block Matching algorithm effects.
Embodiment
To make the object, technical solutions and advantages of the present invention clearer, the present invention is made into one below in conjunction with accompanying drawing
It is described in detail on step ground.
Flowage structure figure as shown in Figure 1.
A kind of tight frame Grouplet associated domain computational methods of the embodiment of the present invention, comprise the following steps：1. input is schemed
Picture is simultaneously converted into gray level image I；2. Grouplet decomposition is carried out, j layer coefficients layer coefficients are calculated, is calculated most by line detective operators
Excellent match point simultaneously calculates j layers of associated domain layer coefficients；3. 2. repetitive cycling step, finishes until J layer coefficients are all calculated.
For the grayscale map I that size is M × N, J layers of tight frame Grouplet passes will be obtained after line detective operators algorithm
Join domain layer coefficients A_{j}(J+1) coefficient layer coefficients { d_{j}[m],a_{J}[m]}_{1≤j≤J}。
Explanation:(1)OB_N45：Size is 3X345 ° of detection template matrixes；OB_0:Size is 3X3 0 ° of detection template
Matrix；OB_P45：Size is 3X3+45 ° of detection template matrixes；(2)Q_{m,n}Represent m rows, the point Q of n row, it is assumed that current point is
P_{m,n}, then point to be matched is Q_{k,(n1)}, wherein k ∈ { m1, m, m+1 }, point from top to bottom successively with pattern matrix OB_N45,
OB_0 is corresponding with OB_P45.(3) J is that total number of plies is decomposed in tight frame Grouplet conversion；Gather { d_{j}[m],a_{J}[m]}_{1≤j≤J}Storage
(J+1) layer coefficients number of plies value；Matrix A_{j}Store jth layer associated domain number of plies value.As shown in Figure 2.
Specific steps：
(1) j=1, { d are initialized_{j}[m],a_{J}[m]}_{1≤j≤J}=0, A_{j}=0, m=n=1；
(2)P_{m,n}Centered on, choose data matrix BP_{3×3}；
(3) binaryzation matrix B P:The average for remembering matrix B P is Av, thenWherein
Positive integer x, y ∈ [1,3]；
(4) corresponding data in BP and line detection template matrix OB_N45, OB_0, OB_P45 is multiplied respectively, and will be multiplied
As a result asked after adding up and thoroughly deserve T1, T2, T3；Point to be matched corresponding to min { T1, T2, T3 } is match point.
(5) point P is calculated_{m,n}Locate associated domain layer coefficients A_{j}(m, n) and coefficient layer coefficients { d_{j}[P],a_{J}[P]}_{1≤j≤J}.It is specific by
Below equation is obtained, and its midpoint Q is point P Optimum Matching point：
Associated domain layer coefficients：
A_{j}[P]=QP
Coefficient layer factor v is updated by below equation：
If m<M, with regard to m=m+1；
Otherwise
If n<N
With regard to n=N+1；
M=1；
Otherwise m=1；N=1；
J=j+1；
If j>J, end loop obtains associated domain layer coefficients A_{j}With coefficient layer coefficients { d_{j}[m],a_{J}[m]}_{1≤j≤J}Otherwise jump
Go to step (2).
Example is tested under Matlab R2014a environment, the associated domain that line detection algorithms proposed by the present invention are calculated
Layer coefficients are the same with Block Matching algorithms, can show the trend of geometry flow in original image.From Fig. 3 correction data
In it is apparent that, in unobvious loss coefficient layer coefficients degree of rarefication, and can be with perfect reconstruction original image on the premise of,
Will be significantly lower than the time required to line detective operators matching algorithm proposed by the present invention using Block Matching algorithms when
Between.
Above disclosed is only a kind of preferred embodiment of the invention, can not limit the power of the present invention with this certainly
Sharp scope, therefore the equivalent variations made according to the claims in the present invention, still belong to the scope that the present invention is covered.
Claims (4)
1. a kind of tight frame Grouplet associated domain computational methods, it is characterised in that comprise the following steps：
1) grayscale map is converted into the image of input；
2) the grayscale map Grouplet is decomposed, calculates jth layer coefficients layer coefficients, Optimum Matching is calculated by line detective operators
Put and calculate jth layer associated domain layer coefficients, wherein 1≤j≤J；
3) repetitive cycling step 2), finished until J layer coefficients are all calculated, wherein J is setting value.
2. tight frame Grouplet associated domain computational methods according to claim 1, it is characterised in that the line detection is calculated
3 × 3 matrix that subtemplate is used.
3. tight frame Grouplet associated domain computational methods according to claim 2, it is characterised in that the line detection is calculated
Son calculate Optimum Matching point formula be：
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WhereinBe withCentered on 3 × 3 image data matrixs after binaryzation；OP_{i}[j] is the line detection for detecting direction i
Operator, i represents to gather a direction in {+45 °, 0 °, 45 ° }；J represents jth of position of homography.
4. tight frame Grouplet associated domain computational methods according to claim 3, it is characterised in that the line detection is calculated
Son calculate Optimum Matching point the step of be：
1) in jth layer, with pointCentered on, it is 3 × 3 to choose size, the data matrix BP that pixel value is constituted,
2) data matrix BP described in binaryzation_{3×3}, note matrix B P_{3×3}Average be Av, then work as BP_{x,y}During less than for the average Av,
BP_{x,y}Value 0, is otherwise 1,
3) formula for calculating Optimum Matching point using the line detective operators is tried to achieve a littleOptimum Matching point m,
4) associated domain layer coefficients are calculatedWith coefficient layer coefficients { d_{j}[m],a_{J}[m]}_{1≤j≤J},
Associated domain layer coefficients：
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When j increases from 1 to J, coefficient layer factor v is updated by below equation：
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Wherein, a [m] represents the equal value coefficient at point m, as j=1, and a [m] is the pixel value at point m；d_{j}[m] is represented at point m
Difference coefficient；S [m] represents the size of support frame at point m, as j=1, s [m]=1.As j=J, a_{J}[m] is by formulaCalculating is obtained；
5) repetitive cycling abovementioned steps, until 1 to J layer of institute, a little all matching is completed.
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Citations (2)
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US8699852B2 (en) *  20111010  20140415  Intellectual Ventures Fund 83 Llc  Video concept classification using video similarity scores 
CN104240201A (en) *  20140904  20141224  南昌航空大学  Fracture image denoising and enhancing method based on groupcontour wavelet transformation 

2017
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Patent Citations (2)
Publication number  Priority date  Publication date  Assignee  Title 

US8699852B2 (en) *  20111010  20140415  Intellectual Ventures Fund 83 Llc  Video concept classification using video similarity scores 
CN104240201A (en) *  20140904  20141224  南昌航空大学  Fracture image denoising and enhancing method based on groupcontour wavelet transformation 
NonPatent Citations (5)
Title 

ALDO MAALOUF 等: "A groupletbased reduced reference image quality assessment", 《2009 INTERNATIONAL WORKSHOP ON QUALITY OF MULTIMEDIA EXPERIENCE》 * 
ALDO MAALOUF 等: "Groupletbased color image superresolution", 《2009 17TH EUROPEAN SIGNAL PROCESSING CONFERENCE》 * 
周志宇: "基于Grouplet变换的金属断口图像处理方法研究", 《中国优秀硕士学位论文全文数据库信息科技辑》 * 
孙熠 等: "基于Grouplet熵与关联向量机的断口图像识别方法研究", 《失效分析与预防》 * 
张玉庆: "智能交通系统中车牌定位问题的研究", 《中国优秀博硕士学位论文全文数据库(硕士)工程科技Ⅱ辑》 * 
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