CN102663707A - Spline surface based image reconstruction method and device thereof - Google Patents
Spline surface based image reconstruction method and device thereof Download PDFInfo
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Abstract
The invention discloses a spline surface based image reconstruction method and a device thereof. The method includes arranging the quantities and locations of sampling points according to a preset scaling; acquiring a node interval where the i<th> sampling point is located according to a coordinate of a node at the N<th> row or column on a spline surface; looking up a quadrilateral domain which surrounds the i<th> sampling point and has a smallest area in the range of the node interval; and reconstructing a pixel value of the i<th> sampling point according to pixel values of four nodes of the quadrilateral domain when the quadrilateral domain exists. By means of the spline surface based image reconstruction method and the device thereof, modifications can be performed on the basis of free form deformation, the accuracy can be adjusted by means of different quantities of control points, a defect that the scaling and shearing of images in free form deformation are non-uniform is overcome, and the efficiency of image reconstruction is improved due to the application of a high-efficiency search strategy.
Description
Technical field
The present invention relates to image processing techniques, particularly relate to image reconstructing method and device based on spline surface.
Background technology
The anamorphose algorithm is as the application of a kind of computer vision aspect, and the anamorphose problem receives much attention in recent years, constantly improves and development during its value is being studied and put into practice.Our application image deformation technology can generate the smooth excessiveness from a width of cloth digital picture to another width of cloth digital picture, perhaps according to the conversion of between multiple image, carrying out, can produce surprising visual effect.The morphing application surface is very wide, and aspect medical image, a kind of especially important application of anamorphose need be simulated a lot of models for some operations, and the hypothesis of distortion, and this technology is very helpful for medical research.It also can be used in the virtual role in the film and tv industry, the role, and amusement, the memory of people's face is synthetic; Virtual realities etc., application common in the film perhaps produce happiness for some given images by it like the variation from some faces to another face; Anger, sorrow, the expression shape change that pleasure etc. are complicated.In fact, being produced on of film trick effect widely applied on the image transformation technical foundation, also obtained great success.These application can be so that image Animando more makes effect more true to nature.We also can be through the change in shape of control object simultaneously, and perhaps the continued operation of the variation of figure action realizes animation effect.
The technology that foremost deformation method is based on grid is the distortion of free form.Nowadays FFD (Free Form Deform, FFD) technology is applied in the certain methods of business software widely.In FFD, a figure is embedded in the grid, comes controlled deformation through the reference mark of mobile grid.FFD is simple to be used easily, but makes the test picture receive nonuniformity convergent-divergent and shearing easily, and this is to hinder bandwagon effect on plurality of applications.And the search of the RP of resampling is difficult, has had a strong impact on the efficient of image reconstruction.
Summary of the invention
Based on this, being necessary provides a kind of image reconstructing method and device based on spline surface to the problems referred to above, can improve the efficient of image reconstruction.
A kind of image reconstructing method of spline surface comprises:
Arrange sampled point quantity and position according to preset scaling;
The coordinate of or row node capable according to the N of spline surface obtains between the node area at i sampled point place;
In the scope between said node area, search around four minimum edge regions of the area of said i sampled point;
When having said four edge regions, according to the pixel value of the said i sampled point of pixel value reconstruct of four nodes of said four edge regions.
Correspondingly, a kind of image reconstruction device based on spline surface comprises:
The sampled point unit of deploying to ensure effective monitoring and control of illegal activities is used for arranging sampled point quantity and position according to preset scaling;
Choose the unit with the said sampled point interval that the unit links to each other of deploying to ensure effective monitoring and control of illegal activities, be used for the coordinate of the capable or row node of N according to spline surface, obtain between the node area at i sampled point place;
Choose the regional acquiring unit that the unit links to each other with said interval, be used in the scope between said node area, search around four minimum edge regions of the area of said i sampled point;
With the node reconfiguration unit that said regional acquiring unit links to each other, be used for pixel value according to the said i sampled point of pixel value reconstruct of four nodes of said four edge regions.
Embodiment of the present invention has following beneficial effect:
The present invention improves on the basis of FFD; And it is adjustable according to preset scaling layout sampled point quantity and position realization precision; Overcome the deficiency that image among the FFD receives nonuniformity convergent-divergent and shearing, used search strategy efficiently to improve the efficient of image reconstruction.
Description of drawings
Fig. 1 is the process flow diagram that the present invention is based on the image reconstructing method of spline surface;
Fig. 2 is the embodiment synoptic diagram that the present invention is based on the image reconstructing method of spline surface;
Fig. 3 is the embodiment process flow diagram that the present invention is based on the image reconstructing method of spline surface;
Fig. 4 is the image reconstruction schematic representation of apparatus that the present invention is based on spline surface;
Fig. 5 is the embodiment synoptic diagram that the present invention is based on the image reconstruction device of spline surface.
Embodiment
For making the object of the invention, technical scheme and advantage clearer, will combine accompanying drawing that the present invention is done to describe in detail further below.
Fig. 1 is the process flow diagram that the present invention is based on the image reconstructing method of spline surface, comprising:
S101: arrange sampled point quantity and position according to preset scaling;
S102: the coordinate of or row node capable according to the N of spline surface, obtain between the node area at i sampled point place;
S103: in the scope between said node area, search around four minimum edge regions of the area of said i sampled point;
S104: when having said four edge regions, according to the pixel value of the said i sampled point of pixel value reconstruct of four nodes of said four edge regions.
Image reconstruction technique generally adopts reference mark control spline surface, comprises the node of reference mark and curved surface in the image, and the FFD technology corresponds to the pixel value of image on the node of curved surface, and obtains the image after the distortion through image resampling.Wherein can through the reference mark number how much realize that precision is adjustable, the number at reference mark is many more, precision is high more, otherwise then low more.But the search of the RP of resampling is difficult, causes the inefficiency of image reconstruction.The image reconstructing method based on spline surface that the present invention proposes improves on the basis of FFD; And it is adjustable according to preset scaling layout sampled point quantity and position realization precision; Overcome the deficiency that image among the FFD receives nonuniformity convergent-divergent and shearing, used search strategy efficiently to improve the efficient of image reconstruction.
At first, arrange sampled point quantity and position according to preset scaling.When this scaling less than 1 the time, carry out the resampling that image dwindles, at this moment, the sum of sampled point lacks than the sum of image node, the location interval between the sampled point is also thinner, might exist some node not to be used; When this scaling greater than 1 the time, carry out the resampling that image amplifies, at this moment, the sum of sampled point is more than the sum of image node, the location interval between the sampled point is also closeer, might exist the reconstructed pixel of some sampled point sampled point adjacent thereto identical.
Then, the coordinate of or row node capable according to the N of spline surface obtains between the node area at i sampled point place.Node in the spline surface has the corresponding relation of arranged.For example, the sampling curved surface for four lines three row then has 12 nodes, and the horizontal ordinate of these 12 nodes (ordinate) is the ordered sequence of irregular alternation on every row (row).Compare according to the horizontal ordinate (ordinate) of said i sampled point and ordered sequence, obtain between the node area at said i sampled point place [M, M+1], be the closed interval between M node and (M+1) individual node between this node area.
Secondly, between said node area, in the scope of [M, M+1], search around four minimum edge regions of the area of said i sampled point.As previously mentioned, because the node in the spline surface has the corresponding relation of arranged, so all there is M position and (M+1) position node in each row (row).When said i sampling optimization between the node area of certain row (row) when [M, M+1], certainty, this i sampling optimization [M ', M '+1] between another node area of certain row (OK).The irregular quadrilateral that is surrounded by said M position node, said (M+1) position node, said M ' position node, said (M '+1) position node is four edge regions of being asked.
At last, when having said four edge regions, according to the pixel value of the said i sampled point of pixel value reconstruct of four nodes of said four edge regions.For example,, can adopt the bilinear interpolation algorithm, the pixel value of above-mentioned four nodes carried out the reconstruct of said sampled point in order further to improve image efficient.Again for example,, can adopt and close on interpolation algorithm most, the pixel value of above-mentioned four nodes carried out the reconstruct of said sampled point in order to obtain better pictures reconstruct effect.
Fig. 2 is the embodiment synoptic diagram that the present invention is based on the image reconstructing method of spline surface.Compared to Figure 1, Fig. 2 is the synoptic diagram of the specific embodiment of the invention.
Fig. 3 is the embodiment process flow diagram that the present invention is based on the image reconstructing method of spline surface.Compare with Fig. 2, Fig. 3 is the method flow diagram of this embodiment.Below in conjunction with Fig. 2, Fig. 3 the present invention is done further detailed description.
S201: arrange sampled point quantity and position according to preset scaling;
Wherein, row (row) number that sampled point is set is K, and the initial value of K is 1, promptly carries out the reconstruct of sampled point since first row (row);
S202: the coordinate of or row node capable according to the N of spline surface, obtain between the node area at i sampled point place;
S203: in the scope between said node area, search around four minimum edge regions of the area of said i sampled point;
Judge whether to exist said four edge regions, when existing, change S204 over to; Otherwise, judge whether (N+1) is last column (row), when not, search (N+1) row or (N+1) row, return S202; Otherwise, judge whether present node is last node, when not, search (i+1) node of current line (row), return S202; Otherwise, judge whether to be last column (row), when current sampling point is not in the end during delegation's (row), carry out the reconstruct of next line sampled point; Otherwise, finish.
S204: according to the pixel value of the said i sampled point of pixel value reconstruct of four nodes of said four edge regions;
Judge whether present node is last node, when not, search (i+1) node of current line (row), return S202; Otherwise, judge whether to be last column (row), when current sampling point is not in the end during delegation's (row), carry out the reconstruct of next line sampled point; Otherwise, finish.
Present embodiment adopts the Biquadratic B-spline curved surface.Preferably, in order to simplify the calculating process of FFD, its function adopt Q (u, v)=UBPB ' V.Wherein, (u v) is the node coordinate value of trying to achieve to Q.U, V leave corresponding to horizontal stroke, the advance of each pixel.P is the reference mark matrix, and B is a parameter matrix, U={u
2, u, 1}; V={v
2, v, 1}; The normalization distance at u, the v reference mark, the upper left corner (being p0,0) that is the curved surface node in the matrix P of reference mark.
In order to reconstruct image, must the pixel of the irregular alignment among the figure be resampled, be illustrated in figure 2 as a part of image of intercepting, mesh lines intersection point wherein is sampled point.Because image reconstruction is image to be done local convergent-divergent handle, so need find out the position and the pixel value of four nodes around each sampled point (promptly among the figure near four pixels of said sampled point).
When the line number of said spline surface during less than columns, adopt binary chop, according to the order of the capable node horizontal ordinate of the N of spline surface, relatively the horizontal ordinate under the horizontal ordinate of i sampled point is interval, obtains between the node area at said i sampled point place;
When the columns of said spline surface during less than line number, adopt binary chop, according to the order of the node ordinate of the N row of spline surface, relatively the ordinate under the ordinate of i sampled point is interval, obtains between the node area at said i sampled point place.
With Fig. 2 is example, and the row coordinate of every row of the image after the distortion increases progressively to the right, and the row-coordinate of every row is to increase progressively downwards, thus can be with line search or row search, the line number of supposing image in the present embodiment is less than columns, and then present embodiment is example with the line search.
Concrete search procedure is following: search since the sampled point A point of first row first row, obtain four edge regions that comprise the area minimum that A orders, according to the pixel value reconstruct A point of four nodes on this four edge regions; Then, the sampled point B point of the reconstruct first row secondary series; The tertial sampled point C point of reconstruct first row ... so analogize all sampled points of reconstruct first row; Then, the sampled point of reconstruct second row first row ... the sampled point D point of reconstruct second row the 4th row is so analogized the B-spline surface image that reconstruct is shown in Figure 2.
Be elaborated in the face of more representative sampled point X, the sampled point Y in position among Fig. 2 down.
Sampled point X is positioned at the junction of four top edge regions and following four edge regions; Because present embodiment is since the first row search; Search for second row then, search the third line ... so, after searching for successfully in the superincumbent quadrilateral of X point; Just no longer down searched for, that is to say that four top edge regions are around four minimum edge regions of the area of said sampled point X.
Search four edge regions at sampled point Y place:
As shown in Figure 2, arrange sampled point quantity and position according to preset scaling.Wherein, the position of sampled point Y is Y (8,3); Suppose that the position of four nodes of sampled point Y periphery is: the 8th node M 1 of second row (7.5,2.2), the 9th node M 2 (8.5 of second row; 2.3), the 8th node M 3 of the third line (7.3,3.1), the 9th node M 4 of the third line (8.3,3.3).
Because the line number of the spline surface of Fig. 2 less than columns, since first row, adopts line search.According to the order of node horizontal ordinate of first row of spline surface, relatively the horizontal ordinate under the horizontal ordinate " 8 " of sampled point Y is interval, obtains between the node area at said sampled point Y place.Preferably; Adopt binary chop: embodiment as shown in Figure 2; First row has ten nodes, and the horizontal ordinate of sampled point Y is compared with the horizontal ordinate of the 5th node, can know that sampled point Y is between the 5th node and the tenth node; Also there is other node between the 5th and the tenth, so continue binary search; The horizontal ordinate of sampled point Y is compared with the horizontal ordinate of the 8th node, can be known that between the 8th node and the tenth node, also there is other node in sampled point Y between the 8th and the tenth, so continue binary search; The horizontal ordinate of sampled point Y is compared with the horizontal ordinate of the 9th node; Can know that sampled point Y is between the 8th node and the 9th node; There is not other node between the 8th and the 9th, so to obtain between the node area at sampled point Y place be between the 8th node of first row and the 9th node.Similarly, can also select alternate manners such as bubble sort algorithm to carry out searching between node area.But above-mentioned binary chop is quick than other algorithm, has more high-level efficiency.
In the scope between said node area, search by the 8th node of first row, the 9th node of first row, the 8th node of second row, the minimum zone of the 9th area that node surrounds of second row, obviously, sampled point Y is outside this zone.So there are not said four edge regions.
When not having said four edge regions, judge whether second row is last column, obviously is not, so N=N+1.Coordinate according to the second row node of spline surface obtains between the node area at said sampled point Y place.In like manner, continue the above-mentioned binary chop of utilization and carry out searching between node area, final acquisition point sampled point Y belongs between the node area of [M1, M2].
In the scope between the node area of said [M1, M2], search the four minimum edge regions of the area that centers on said sampled point Y that acquisition is made up of M1 as shown in Figure 2, M2, M3, M4.
When having said four edge regions, the pixel value according to the pixel value reconstructed sample point Y of four nodes such as the M1 of said four edge regions, M2, M3, M4 preferably, can adopt the bilinear interpolation algorithm to calculate.
Judge that whether said sampled point Y is last node, obviously is not, so i=i+1.Again since first row,, search four edge regions that sampled point Z belongs to according to the coordinate of the first row node of spline surface.The pixel value of reconstructed sample point Z.Because sampled point Z is last node of current line, whether is positioned at last column so judge it again, obviously is not, so K=K+1 carries out the reconstruct of next line sampled point.
According to the coordinate of two interval nodes of the respective nodes of the coordinate of two nodes between the capable node area of the coordinate of said i sampled point, said N, (N+1) row, the preset linear restriction model of utilization is searched around the minimum zone of the area of said i sampled point.
In order to search apace around four minimum edge regions of the area of said i sampled point, the present invention uses linear programming principle, and a kind of determination methods of simply searching is provided.Owing to need ultra four edge regions of looking for the area minimum, search for so adopt between the node area between adjacent two row (row); Because whether need to judge four edge regions of searching around said i sampled point, so, use preset linear restriction model to judge.In addition, the present invention does not get rid of can utilize the identical determine effect of other similar judgment formulas acquisitions, but the needs supplementary notes is that linear restriction model algorithm provided by the invention is simple, is convenient to computer realization, helps to improve the efficient of image reconstruction.
Continuing above example, to search four edge regions of a Y be example.The linear restriction model that the present invention presets is:
The coordinate of said i sampled point (as; The coordinate of two nodes between the node area that the coordinate of sampled point Y), said N is capable (as; M1, M2 node), the coordinate of interval two nodes of the respective nodes of (N+1) row (as, M3, M4 node), then must satisfy simultaneously:
(x1-x3)(y2-y3)-(y1-y3)(x2-x3)≥0
(x1-x4)(y3-y4)-(y1-y4)(x3-x4)≥0
(x1-x5)(y4-y5)-(y1-y5)(x4-x5)≥0
(x1-x2)(y5-y2)-(y1-y2)(x5-x2)≥0
Or, satisfy simultaneously:
(x1-x2)(y3-y2)-(y1-y2)(x3-x2)≤0
(x1-x3)(y4-y3)-(y1-y3)(x4-x3)≤0
(x1-x4)(y5-y4)-(y1-y4)(x5-x4)≤0
(x1-x2)(y2-y5)-(y1-y5)(x2-x5)≤0
Then judge and to be in zone (M1M2M3M4): around four minimum edge regions of the area of said i sampled point.
If be terminated up to search and also be much to seek RP, then the pixel value of this sampled point is endowed a certain specific value.
It is example that present embodiment adopts B-spline surface, because B-spline surface is a kind of curved surface very flexibly, the local shape of curved surface receives the control of respective vertices very directly perceived.If these summit control technologys are used well, can make whole B-spline surface satisfy some special technique requirements at some position.The characteristic of hence one can see that B-spline surface satisfies the operational requirements that in the inventive method pixel is adjusted accordingly well.What need supplementary notes is, the present invention also can adopt other spline surfaces to carry out image reconstruction, but it is more excellent to adopt B-spline surface to carry out the effect of reconstruct.
When said scaling ratio greater than 1 the time, image is carried out over-sampling.
Because what adopt is image resampling, the present invention also carries out over-sampling to the needs enlarged image, can reach the effect of the FSAA processing that realizes image.In addition, the method that B-spline surface combines with image resampling goes for any distortion of image.
Further realize better pictures reconstruct effect if desired, can use more the complex image interpolation method (like, two cubes of method of interpolation; The B spline method, S spline method etc.) realize image reconstruction, can increase very big algorithm complex; Better effects if, but speed is slow slightly.
Fig. 4 is the image reconstruction schematic representation of apparatus that the present invention is based on spline surface, comprising:
The sampled point unit of deploying to ensure effective monitoring and control of illegal activities is used for arranging sampled point quantity and position according to preset scaling;
Choose the unit with the said sampled point interval that the unit links to each other of deploying to ensure effective monitoring and control of illegal activities, be used for the coordinate of the capable or row node of N according to spline surface, obtain between the node area at i sampled point place;
Choose the regional acquiring unit that the unit links to each other with said interval, be used in the scope between said node area, search around four minimum edge regions of the area of said i sampled point;
With the node reconfiguration unit that said regional acquiring unit links to each other, be used for pixel value according to the said i sampled point of pixel value reconstruct of four nodes of said four edge regions.
Fig. 4 is corresponding with Fig. 1, identical in the method for operation of above-mentioned each unit and the method.
Fig. 5 is the embodiment synoptic diagram that the present invention is based on the image reconstruction device of spline surface.
As shown in Figure 5, in one embodiment, also comprise:
With the regional search unit that said regional acquiring unit links to each other, be used for obtaining between the node area at said i sampled point place according to each row of spline surface or the coordinate of row node.
As shown in Figure 5, in one embodiment, also comprise:
Be connected said regional search unit and the said sampled point sampled point selected cell between the unit of deploying to ensure effective monitoring and control of illegal activities, be used for selecting next sampled point according to the result who searches said four edge regions.
In one embodiment, said regional acquiring unit comprises:
The constraint judging unit; Be used for the coordinate of two interval nodes of the respective nodes of coordinate, (N+1) row of two nodes between the capable node area of the coordinate according to said i sampled point, said N, the preset linear restriction model of utilization is searched around the minimum zone of the area of said i sampled point.
Fig. 5 is corresponding with Fig. 3, identical among the figure in the method for operation of each unit and the method.
Through the description of above embodiment, those skilled in the art can be well understood to the present invention and can realize by the mode that software adds essential hardware platform, can certainly all implement through hardware.Based on such understanding; All or part of can the coming out that technical scheme of the present invention contributes to background technology with the embodied of software product; This computer software product can be stored in the storage medium, like ROM/RAM, magnetic disc, CD etc., comprises that some instructions are with so that a computer equipment (can be a personal computer; Server, the perhaps network equipment etc.) carry out the described method of some part of each embodiment of the present invention or embodiment.
The above embodiment has only expressed several kinds of embodiments of the present invention, and it describes comparatively concrete and detailed, but can not therefore be interpreted as the restriction to claim of the present invention.Should be pointed out that for the person of ordinary skill of the art under the prerequisite that does not break away from the present invention's design, can also make some distortion and improvement, these all belong to protection scope of the present invention.Therefore, the protection domain of patent of the present invention should be as the criterion with accompanying claims.
Claims (10)
1. the image reconstructing method based on spline surface is characterized in that, comprising:
Arrange sampled point quantity and position according to preset scaling;
The coordinate of or row node capable according to the N of spline surface obtains between the node area at i sampled point place;
In the scope between said node area, search around four minimum edge regions of the area of said i sampled point;
When having said four edge regions, according to the pixel value of the said i sampled point of pixel value reconstruct of four nodes of said four edge regions.
2. the image reconstructing method based on spline surface according to claim 1 is characterized in that:
When not having said four edge regions,, obtain between the node area at said i sampled point place according to the next line of spline surface or the coordinate of row node;
In the scope between said node area, search around four minimum edge regions of the area of said i sampled point.
3. the image reconstructing method based on spline surface according to claim 2 is characterized in that:
When each coordinate capable or each row node according to said spline surface; Search around four minimum edge regions of the area of said i sampled point; When all not having said four edge regions, the coordinate of or row node capable according to the N of spline surface obtains between the node area at next sampled point place; In the scope between this node area, search around four minimum edge regions of the area of said next sampled point.
4. according to each described image reconstructing method of claim 1 to 3, it is characterized in that the coordinate of or row node capable according to the N of spline surface obtains the step between the node area at i sampled point place, comprising based on spline surface:
When the line number of said spline surface during less than columns, adopt binary chop, according to the order of the capable node horizontal ordinate of the N of spline surface, relatively the horizontal ordinate under the horizontal ordinate of i sampled point is interval, obtains between the node area at said i sampled point place;
When the columns of said spline surface during less than line number, adopt binary chop, according to the order of the node ordinate of the N row of spline surface, relatively the ordinate under the ordinate of i sampled point is interval, obtains between the node area at said i sampled point place.
5. according to each described image reconstructing method of claim 1 to 4, it is characterized in that, in the scope between said node area, search, also comprise around the step of four minimum edge regions of the area of said i sampled point based on spline surface:
According to the coordinate of two interval nodes of the respective nodes of the coordinate of two nodes between the capable node area of the coordinate of said i sampled point, said N, (N+1) row, the preset linear restriction model of utilization is searched around the minimum zone of the area of said i sampled point.
6. according to each described image reconstructing method of claim 1 to 5, it is characterized in that the step according to preset scaling is arranged sampled point quantity and position comprises based on spline surface:
When said scaling ratio greater than 1 the time, image is carried out over-sampling.
7. the image reconstruction device based on spline surface is characterized in that, comprising:
The sampled point unit of deploying to ensure effective monitoring and control of illegal activities is used for arranging sampled point quantity and position according to preset scaling;
Choose the unit with the said sampled point interval that the unit links to each other of deploying to ensure effective monitoring and control of illegal activities, be used for the coordinate of the capable or row node of N according to spline surface, obtain between the node area at i sampled point place;
Choose the regional acquiring unit that the unit links to each other with said interval, be used in the scope between said node area, search around four minimum edge regions of the area of said i sampled point;
With the node reconfiguration unit that said regional acquiring unit links to each other, be used for pixel value according to the said i sampled point of pixel value reconstruct of four nodes of said four edge regions.
8. the image reconstruction device based on spline surface according to claim 7 is characterized in that, also comprises:
With the regional search unit that said regional acquiring unit links to each other, be used for obtaining between the node area at said i sampled point place according to each row of spline surface or the coordinate of row node.
9. the image reconstruction device based on spline surface according to claim 8 is characterized in that, also comprises:
Be connected said regional search unit and the said sampled point sampled point selected cell between the unit of deploying to ensure effective monitoring and control of illegal activities, be used for selecting next sampled point according to the result who searches said four edge regions.
10. according to each described image reconstruction device of claim 7 to 9, it is characterized in that said regional acquiring unit comprises based on spline surface:
The constraint judging unit; Be used for the coordinate of two interval nodes of the respective nodes of coordinate, (N+1) row of two nodes between the capable node area of the coordinate according to said i sampled point, said N, the preset linear restriction model of utilization is searched around the minimum zone of the area of said i sampled point.
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