CN106060509A - Free viewpoint image synthetic method introducing color correction - Google Patents
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- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04N13/00—Stereoscopic video systems; Multi-view video systems; Details thereof
- H04N13/10—Processing, recording or transmission of stereoscopic or multi-view image signals
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- H—ELECTRICITY
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- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N13/00—Stereoscopic video systems; Multi-view video systems; Details thereof
- H04N13/10—Processing, recording or transmission of stereoscopic or multi-view image signals
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Abstract
The invention discloses a free viewpoint image synthetic method introducing color correction and mainly solves the problems in an existing viewpoint synthetic technology that a synthetic image is discontinuous in color and is fuzzy in cavity edge. The method comprises the realization steps of: input left and right viewpoint views and corresponding depth images thereof, and by means of 3D conversion, obtaining left and right virtual views; according to a position relation, synthesizing the left and right virtual views into a non-blocked region of an intermediate virtual view; utilizing color differences among background regions of the virtual view to replace the color differences among blocked regions of the virtual view, and utilizing a histogram matching algorithm to obtain the blocked regions subjected to color correction; fusing the non-blocked region and the blocked regions subjected to color correction, and obtaining an intermediate viewpoint image having hollow points; and carrying out cavity filling layer by layer on the intermediate viewpoint image having the hollow points, and obtaining a final synthetic virtual image. According to the invention, the quality of the synthetic virtual image is improved, the watching comfort of a 3D video is improved, and the method can be applied to stereo multimedia.
Description
Technical field
The invention belongs to technical field of image processing, particularly to a kind of free view-point image combining method, can be used for standing
Body multimedia.
Background technology
Owing to three-dimensional multimedia 3DTV can provide more true nature, lifelike visual environment, in recent years by extensively
Big consumer knows accreditation and progressively penetrates into multimedia market, and free view-point synthesis is vast as the core technology in 3DTV field
Scientific research personnel's Learning Studies.Owing to viewing location is required by 3D video, i.e. diverse location should receive different regarding
Difference i.e. depth information, this means that each viewing location should have a corresponding seat in the plane to carry out scene capture, due to
Stereoscopic camera cost is high and shooting condition limits, the most difficult realization of omnidirectional shooting.The synthesis of free view-point image effectively solves
This technical barrier, the development to rich solid video resource and 3DTV field is most important.
Free view-point image synthesizes based on left and right viewpoint view and range image integration intermediate-view virtual view thereof
One technology.In synthesis new viewpoint image process, due to the conversion of viewpoint, Partial occlusion region can be exposed in the visual field again,
This is accomplished by left and right visual point image and carries out synthesizing to fill up new exposed region.2009, Y.Mori et al. proposed first and compares
The free view-point synthetic method of scientific system (Y.Mori, N.Fukushima, T.Yendo, T.Fujii and M.Tanimoto,
View generation with 3D warping using depth information for FTV,Signal
Processing:Image Communication, 24,65 72,2009) following four steps, it are broadly divided into:
1) 3D conversion: 3D converts the process substantially projected, and Mori uses forward projection's mode, becomes based on space
Change relation, left and right view and depth image thereof are projected to target view position, obtain the virtual left and right view of target view.
2) round-off error removes: during 3D forward projection, and round up the virtual view after causing conversion to coordinate
The disappearance of middle one pixel of appearance, needs detect error pixel and fill with surrounding pixel, and then completes error pixel
Removing of point.
3) left and right virtual view synthesis: merge left and right virtual view by position relationship and obtain non-blocking region, and difference
Fill up, with left and right virtual view, the occlusion areas that left and right sides in medial view newly exposes, obtain destination virtual view.
4) image repair: owing to the degree of depth is suddenlyd change, there is also the pixel disappearance of another form, claims in 3D conversion process
Be cavity, need by some image repair algorithms carry out cavity filling.
The problem of round-off error in converting for 3D, K.J.Oh et al. proposes one and is regarded to the left and right by target view position
Backwards projection strategy (K.J.Oh, S.Yea, A.Vetro and Y.S.Ho, the Virtual View of some position projection
Synthesis Method and Self-Evaluation Metrics for Free Viewpoint Television
and 3D Video,International Journal of Imaging Systems and Technology,20,378–
390,2010), round-off error is taken at the viewpoint position coordinate of left and right, it is ensured that it is right that the target view in each non-blocking region has
The mapping pixel answered, the problem that effectively prevent error pixel disappearance.
In the virtual view building-up process of left and right, the occlusion areas owing to exposing to be used left and right view simultaneously and fill out
Mending, and left and right viewpoint shooting condition can not be consistent, there is the difference in color, brightness and saturation in left and right view, this just produces
Give birth to the discontinuous problem of composograph color.For this problem, K.J.Oh et al. it is also proposed a kind of rectangular histogram in the text
Matching algorithm, is considered as front view by one of left and right view, and another is considered as auxiliary view.It is first depending on position relationship, is main regards
Figure and auxiliary view synthetic mesophase virtual view；Then virtual view and the histogram frequency distribution diagram of front view are calculated, by directly
Side's figure coupling, makes virtual view and front view have identical color characteristic；Finally fill up virtual with front view and auxiliary view to regard
Occlusion areas in figure.The method efficiently solves the color discontinuous problem between composograph and front view, but synthesizes
The discontinuous situation of color between image and assistant images is the most fairly obvious.
It is the major issue that free view-point image synthesizes and 2D turns in 3D that cavity is filled.K.J.Oh proposes cavity from the back of the body
Scape (K.J.Oh, S.Yea and Y.S.Ho, Hole filling method using depth based in-painting
for view synthesis in free viewpoint television and 3-d video,Proc.Picture
Coding Symposium, 14,2009), and isolate prospect background according to depth information, and then carry out sky by background information
Hole is filled, but concrete filling mode not exhaustive and preferable.M.Solh proposes a kind of simple efficient successively cavity filling side
Formula (M.Solh and G.AlRegib, Hierarchical hole-filling for depth-based view
synthesis in FTV and 3D video,IEEE Journal on Selected Topics in Signal
Processing, 6,495 504,2012), its shortcoming is the necessity that have ignored background filling cavity, causes filling regional edge
The consequence that edge is fuzzy.
Summary of the invention
The purpose of the present invention, for the deficiency of above-mentioned prior art, proposes to introduce the free view-point image synthesis of color correction
Method, so that in composograph color discontinuous is completely eliminated, improves the definition filling edges of regions.
The technical scheme is that: carry out View synthesis based on color correction by the left and right view after 3D is converted,
And synthesis virtual view is carried out successively cavity filling based on depth information, obtain color continuous print high-quality intermediate-view and regard
Figure.Its step includes the following:
1) input left and right viewpoint view and the depth map of their correspondences, based on position relationship and projection equation by left and right view
And depth map projects to intermediate-view plane, obtain left virtual view WL, right virtual view WR, left virtual depth figure DLWith right void
Intend depth map DR；
2) by occlusion areas W of left virtual viewL' and occlusion areas W of right virtual viewR' overlap, by left virtual view
Non-blocking region MLNon-blocking region M with right virtual viewROverlap；
3) by the non-blocking region M of left virtual viewLNon-blocking region M with right virtual viewRIt is weighted merging,
Non-blocking region part M to intermediate virtual view；
4) by the non-blocking region N of left virtual depth figureLNon-blocking region N with right virtual depth figureRIt is weighted melting
Close, obtain the non-blocking region N of intermediate virtual depth map；
5) by the non-blocking region N of intermediate virtual depth map, occlusion areas D of left virtual depth figureL' and right virtual depth
Occlusion areas D of figureR' merge, obtain final intermediate virtual depth map A0；
6) the non-blocking region M of middle virtual view is carried out image segmentation, isolate prospect MfWith background Mb；
7) statistics non-blocking regional background and the rectangular histogram of occlusion areas:
7a) by the background area M being partitioned intob, correspondence finds out the background area M in left virtual viewLbWith right virtual view
In background area MRb, make intermediate virtual view background area M respectivelybStatistic histogram Hb, left virtual view background MLb's
Statistic histogram HLbWith right virtual view background MRbStatistic histogram HRb；
7b) make left virtual view occlusion areas WL' statistic histogram HL' and right virtual view occlusion areas WR' statistics
Rectangular histogram HR'；
8) calculate the difference between the rectangular histogram of background area, substitute the difference between occlusion areas rectangular histogram with it, obtain
The statistic histogram C of occlusion areas on the left of intermediate virtual viewLStatistic histogram C with right side occlusion areasR；
9) Histogram Matching algorithm is used, by the statistic histogram H of left virtual view occlusion areasL' it is matched to middle void
Intend the statistic histogram C of occlusion areas on the left of viewL, obtain occlusion areas Cf on the left of the intermediate virtual view after color correctionL,
In like manner by the statistic histogram H of right virtual view occlusion areasR' to be matched to the statistics of occlusion areas on the right side of intermediate virtual view straight
Side figure CR, obtain occlusion areas Cf on the right side of the intermediate virtual view after color correctionR；
10) by the non-blocking region M of intermediate virtual view, left side occlusion areas CfLWith right side occlusion areas CfRMelt
Close, obtain new intermediate virtual view B0；
11) according to virtual depth figure A0In depth information, choose background pixel to itself and intermediate virtual view B0Carry out
Successively down-sampling, obtains each layer down-sampled virtual depth figure AkWith virtual view Bk, until the virtual depth figure A of end layer S layerS
With virtual view BSIn there is no cavity；
12) from the beginning of S layer, down-sampled virtual view B is the most upwards filledk'In cavity, obtain the reparation figure of each layer
As Fk', until obtaining initiation layer to repair image F0, the most final free view-point image.
The present invention compared with prior art has the following characteristics that
1. the present invention uses statistical theory and replacement thought to carry out color correction, closes with medial view is non-with left and right view
The color distortion between color distortion reaction medial view and left and right view occlusion areas between plug region, and then solve synthesis
Color discontinuous problem between virtual view occlusion areas and non-blocking region.
2. the present invention uses image segmentation algorithm, and non-blocking region carries out the segmentation of prospect background, with composograph with
The color distortion between color distortion reaction occlusion areas between view background area, left and right, owing to occlusion areas is from the back of the body
Scape, reacts the color distortion of occlusion areas more accurately rationally with the color distortion of background area.
3. the present invention uses Histogram Matching algorithm, by the Histogram Matching of occlusion areas in the view of former left and right to color school
The rectangular histogram of occlusion areas after just, the occlusion areas image of reconstruct is not only natural, and can hold in the mouth with non-blocking region no color differnece
Connect.
4. the present invention uses successively cavity filling algorithm based on the degree of depth, based on depth information, autotelic selection background
Neighborhood territory pixel point fills vacancy pixel, and this accurate fill method is effectively improved the picture quality of synthesis virtual view.
The simulation experiment result shows, the present invention combine color correction algorithm based on Histogram Matching and based on the degree of depth by
Layer cavity filling algorithm carries out the synthesis of virtual view, can obtain the composograph of true nature, be that one can significantly improve
The free view-point View synthesis algorithm of the system perfecting of viewing comfort level.
Accompanying drawing explanation
Fig. 1 be the present invention realize general flow chart；
Fig. 2 is that in the present invention, sub-process figure is filled in successively cavity based on the degree of depth；
Fig. 3 is the test image used in l-G simulation test；
Fig. 4 is to test set Ballet, with the free view-point image of the present invention and existing two kinds of typical methods synthesis with true
Comparative result between value；
Fig. 5 is to test set Breakdancing, with the present invention and the free view-point figure of existing two kinds of typical methods synthesis
Comparative result between picture and true value.
Detailed description of the invention
Below in conjunction with accompanying drawing, detailed description of the invention and the effect of the present invention are described in further detail:
With reference to Fig. 1, the detailed description of the invention of the present invention is as follows:
Step 1, input left and right viewpoint view and the depth map of their correspondences also carry out 3D conversion.
1a) input the left view point depth map L of left view point view L to be synthesized and its correspondenceD, right viewpoint view R is right with it
The right viewpoint depth map R answeredD；
1b) based on position relationship and projection equation, they are carried out 3D transformation by reciprocal direction, left view point view L is projected to centre
Viewpoint plane obtains left virtual view WL, right viewpoint view R is projected to intermediate-view plane and obtains right virtual view WR, by a left side
Viewpoint depth map LDProject to intermediate-view plane and obtain left virtual depth figure DL, by right viewpoint depth map RDProject to Intermediate View
Point plane obtains right virtual depth figure DR。
Here the left and right viewpoint view used, left and right viewpoint depth map, positional information and projection matrix all derive from micro-
The data base that soft academy provides, Microsoft Research, Image-Based Realities-3D Video
Download,/http://research.microsoft.com/ivm/3DVideoDownload/S。
Step 2, overlap left virtual view and the occlusion areas of right virtual view, and overlap left virtual view and right virtual view
Non-blocking region.
Due to the change at visual angle, the left virtual view W after 3D convertsLWith right virtual view WRWill there is new exposure
Region occurs, does not has the image information of the new exposed region of this part in being originally inputted view, this excalation image information
New exposed region is occlusion areas, if left virtual view has one piece of occlusion areas, the most artificial erase in right view and
The image information of symmetrical region, if right virtual view has one piece of occlusion areas, erase in left view therewith with regard to artificial
The image information of symmetrical region, and then make left virtual view and right virtual view have left virtual view occlusion areas M of coincidenceLWith
Right virtual view occlusion areas MR, also have the left virtual view non-blocking region W of coincidence simultaneouslyL' and right virtual view non-occlusion area
Territory WR'。
Step 3, the non-blocking region M of synthetic mesophase virtual view.
3a) based on the position relationship provided in data base, calculate the left view point geometric distance t to intermediate-viewLRegard with the right side
Point arrives the geometric distance t of intermediate-viewR, according to geometric distance, calculate the process in synthetic mesophase virtual view non-blocking region
In, the weight coefficient α and the weight coefficient 1-α in right virtual view non-blocking region in left virtual view non-blocking region, wherein:
3b) based on weight coefficient, to left virtual view non-blocking region MLWith right virtual view non-blocking region MRAdd
Power merges, and obtains the non-blocking region part of intermediate virtual view: M=α ML+(1-α)·MR。
Step 4, the non-blocking region N of synthetic mesophase virtual depth figure.
According to the weight coefficient α in step 3, by the non-blocking region N of left virtual depth figureLNon-with right virtual depth figure
Occlusion areas NRIt is weighted merging, obtains the non-blocking region N:N=α N of intermediate virtual depth mapL+(1-α)·NR；
Step 5, synthetic mesophase virtual depth figure A0。
Merge the non-blocking region N of intermediate virtual depth map, occlusion areas D of left virtual depth figureL' and right virtual depth
Occlusion areas D of figureR', obtain final intermediate virtual depth map: A0=N+DL'+DR'。
Step 6, carries out image segmentation to the non-blocking region M of middle virtual view.
Foreground and background can not be isolated exactly owing to relying solely on depth information, existing some can be used to compare
Effective image segmentation algorithm, carries out image segmentation to the non-blocking region M of middle virtual view, isolates prospect M accuratelyf
With background Mb.Conventional images dividing method can be found in document:
[1].D.Comaniciu,P.Meer,“Mean shift:a robust approach toward feature
space analysis,”IEEE Transactions on Pattern Analysis and Machine
Intelligence,vol.24,no.5,pp.603–619,2002；
[2].P.Meer,B.Georgescu,“Edge detection with embedded confidence,”IEEE
Trans.Pattern Anal.Machine Intell,vol.2 8,2001；
[3].C.Christoudias,B.Georgescu,P.Meer,“Synergism in low level
vision,”International Conference of Pattern Recognition,2001.
Step 7, statistics non-blocking regional background and the rectangular histogram of occlusion areas.
7a) according to intermediate virtual view background area Mb, correspondence finds out the background area M in left virtual viewLbWith right void
Intend the background area M in viewRb, according to statistics with histogram algorithm, in the statistics interval that pixel value is [0,255], make respectively
Intermediate virtual view background area MbStatistic histogram Hb, left virtual view background MLbStatistic histogram HLbVirtual with the right side regard
Figure background MRbStatistic histogram HRb；
7b) according to statistics with histogram algorithm, in the statistics interval that pixel value is [0,255], make left virtual view inaccessible
Region WL' statistic histogram HL' and right virtual view occlusion areas WR' statistic histogram HR'。
Step 8, makees the left side occlusion areas statistic histogram C of intermediate virtual viewLNogata is added up with right side occlusion areas
Figure CR。
Due to occlusion areas, the image section being namely blocked derives from background, so left view regards with intermediate virtual
The color distortion of figure occlusion areas, can substitute with the color distortion between left view and intermediate virtual view background area,
In like manner right view and the color distortion of intermediate virtual view occlusion areas, can use right view and intermediate virtual view background area
Between color distortion substitute.
8a) with the statistic histogram H of left virtual view backgroundLbDeduct the statistic histogram H of intermediate virtual view backgroundb,
Obtain the statistical average difference value histogram Diff between left virtual view background area and intermediate virtual view background areaL；With
Reason, with the statistic histogram H of right virtual view backgroundRbDeduct the statistic histogram H of intermediate virtual view backgroundb, obtain right void
Intend the statistical average difference value histogram Diff between view background area and intermediate virtual view background areaR；
8b) based on replacement thought above, by statistical average difference value histogram DiffLIt is added in left virtual view occlusion areas
Statistic histogram HLOn ', obtain the statistic histogram C of occlusion areas on the left of intermediate virtual viewL；In like manner, by statistical average
Difference value histogram DiffRIt is added in the statistic histogram H of right virtual view occlusion areasROn ', obtain on the right side of intermediate virtual view
The statistic histogram C of occlusion areasR。
Step 9, carries out color by Histogram Matching to left virtual view occlusion areas and right virtual view occlusion areas
Correct.
9a) pixel value of pixel in left virtual view occlusion areas is projected to left and right adjacent pixels value, to change a left side
The statistic histogram H of virtual view occlusion areasL' so that it is it is equal to the statistics Nogata of occlusion areas on the left of intermediate virtual view
Figure CL, i.e. by Histogram Matching, by the statistic histogram H of left virtual view occlusion areasL' it is matched to an intermediate virtual view left side
The statistic histogram C of side occlusion areasL, obtain left virtual view occlusion areas Cf through color correctionL；
9b) pixel value of pixel in right virtual view occlusion areas is projected to left and right adjacent pixels value, to change the right side
The statistic histogram H of virtual view occlusion areasR' so that it is it is equal to the statistics Nogata of occlusion areas on the left of intermediate virtual view
Figure CR, i.e. by Histogram Matching, by the statistic histogram H of right virtual view occlusion areasR' it is matched to the intermediate virtual view right side
The statistic histogram C of side occlusion areasR, obtain right virtual view occlusion areas Cf through color correctionR。
Step 10, synthetic mesophase virtual view B0。
Based on left virtual view occlusion areas Cf through color correctionL, right virtual view occlusion areas CfRAfter merging
Intermediate virtual view non-blocking region M, synthetic mesophase virtual view: B0=M+CfL+CfR。
Step 11, to middle virtual depth figure A0With intermediate virtual view B0Carry out successively down-sampling.
The successively cavity filling algorithm that M.Solh proposes can be used to fill the cavity in synthesis virtual view, first carries out
Successively down-sampling, obtains the down-sampling virtual view of each layer, then by the bottom, the most upwards repairs the down-sampling of each layer
Virtual view, obtains the reparation image of initiation layer.This algorithm ignores cavity and derives from this advantageous information of background, and obtain repaiies
Complex pattern is in hole region edge blurry, and therefore the present invention adds background information during down-sampling, solves hole region limit
The problem that edge is fuzzy, its step is as follows:
Shown in the filled arrows of reference Fig. 2, being implemented as follows of this step:
11a) according to virtual depth figure A0In depth information, choose background pixel and it carried out successively down-sampling, to
Whole layer does not has empty point, obtains A successively0Each layer down-sampled images A1, A2..., Ak..., AS, wherein kth layer virtual depth
Figure AkIt is based on its last layer virtual depth figure Ak-1Obtain, virtual depth figure AkMiddle any point (m, n) to ask for formula as follows:
Wherein, Xm,nIt is-1 layer of virtual depth figure A of kthk-1In a size be the matrix-block of 5 × 5, its central point (2 ×
M+3,2 × n+3) place；ω is the gaussian kernel of 5 × 5；Qh is used to the threshold value of division prospect and background, and depth value is more than qh
For background, it is prospect less than qh；L (x) function is used for choosing background pixel point,Nz (u) representing matrix u
The number of middle non-zero points；Num (v) represents the element number meeting condition v；The value of k is incremented by S one by one by 1, S be make virtual
Depth map ASIn do not have cavity point end layer.
It is to say, virtual depth figure AkMiddle any point (m, pixel value A n)k(m n), is by virtual depth figure
Ak-1In matrix-block Xm,nCarry out anisotropy smothing filtering based on the degree of depth to try to achieve:
As matrix-block Xm,nIn pixel without cavity point, and when all pixels broadly fall into background or prospect, right
Xm,nIn all pixels carry out Gaussian smoothing, obtain point (m, pixel value A n)k(m,n)；
As matrix-block Xm,nIn containing cavity point, but all non-cavities point is when broadly falling into background or prospect, to Xm,nMiddle institute
There is non-cavity point pixel value to be weighted averagely, obtain point (m, pixel value A n)k(m,n)；
As matrix-block Xm,nIn non-cavity point partly belong to foreground part when belonging to background, choose Xm,nIn had powerful connections
Pixel carries out the weighted average of pixel value, obtains point (m, pixel value A n)k(m,n)；
As matrix-block Xm,nIn pixel distribution be not belonging to above-mentioned three kinds of situations and be, point (m, pixel value A n)k(m,n)
It is zero.
11b) according to virtual depth figure A0In depth information, choose background pixel to virtual view B0Carry out adopting down
Sample, does not has empty point to end layer, obtains B successively0Each layer down-sampled images B1, B2..., Bk..., BS, wherein any kth
Layer virtual view BkIt is based on its last layer virtual view Bk-1Obtain, virtual view BkMiddle any point (m, n) ask for formula
As follows:
Wherein, Ym,nIt is-1 layer of down-sampling virtual view B of kthk-1In a size be the matrix-block of 5 × 5, its central point exists
(2 × m+3,2 × n+3) place, Xm,nStep 11a) in-1 layer of virtual depth figure A of kth of mentioningk-1In matrix-block, its size
With position all with Ym,nCorrespondence, is used for providing depth information, say, that virtual view BkMiddle any point (m, pixel value n)
Bk(m n), is by virtual view Bk-1In matrix-block Ym,nCarry out anisotropy smothing filtering based on the degree of depth to try to achieve, deeply
Degree information is by matrix-block Xm,nThere is provided.
Step 12, successively repairs cavity by up-sampling, obtains final free view-point image F0。
Shown in the hollow arrow of reference Fig. 2, being implemented as follows of this step:
12a) according to the down-sampling virtual view B of S layerSIn do not have cavity characteristic, by the virtual view B of S layerSDeng
It is same as the reparation image F of S layerS, i.e. FS=BS；
12b) by linear interpolation, S layer is repaired image FSUp-sampling, obtain with resolution such as S-1 layers is swollen
Swollen virtual view ES-1, wherein ES-1In be positioned at pth row, q row point (p, pixel value E q)S-1(p q) asks as follows
Take:
Wherein i' only take even number-2, and 0,2}, j' the most only take even number-2, and 0,2}, i', j' be used for choose reparation image FS
In with point (p, q) centered by 3 × 3 matrix-block, by selected 3 × 3 matrix-blocks are carried out based on weight vectorsSmooth filter
Ripple, obtains expanding virtual view ES-1Midpoint (p, pixel value E q)S-1(p,q)；Weight vectorsIt is used for determining above-mentioned reparation image FSThe shared power of each element in matrix-block selected by
Weight:
Work as i'=-2, during j'=-2, elementI.e. FSWeight shared by (p-1, q-1)For:
Work as i'=-2, during j'=0, element FS(p-1, q) shared by weightIt is 0.02；
Work as i'=-2, during j'=2, element FSWeight shared by (p-1, q+1)It is 0.052；
Work as i'=0, during j'=-2, element FSWeight shared by (p, q-1)It is 0.02；
Work as i'=0, during j'=0, element FS(p, q) shared by weightFor: 0.042；
Work as i'=0, during j'=2, element FSWeight shared by (p, q+1)It is 0.02；
Work as i'=2, during j'=-2, element FSWeight shared by (p+1, q-1)It is 0.052；
Work as i'=2, during j'=0, element FS(p+1, q) shared by weightIt is 0.02；
Work as i'=2, during j'=2, element FSWeight shared by (p+1, q+1)It is 0.052。
12c) with expanding virtual view ES-1In pixel, fill same layer virtual view BS-1Pixel at middle cavity
Point, obtains the reparation image F of S-1 layerS-1, wherein FS-1In be positioned at pth row, q row point (p, pixel value F q)S-1(p q) presses
Equation below is asked for:
Step 12b) and 12c) give and be transitioned into S-1 layer by S layer, and obtain S-1 layer and repair image FS-1Process, under
Face this process is applied to any kth ' layer, and obtain the reparation image F of k'-1 layerk'-1；
12d) by step 12b), to any kth ' layer repairs image Fk'Up-sample, obtain differentiating with k'-1 layer etc.
The expansion virtual view E of ratek'-1, then by step 12c) with expanding virtual view Ek'-1In pixel fill same layer virtual
View Bk'-1Pixel at middle cavity, obtains the reparation image F of k'-1 layerk'-1, the value of k' is successively decreased to 0 one by one by S-1, i.e.
By S-1 layer, the most upwards repetitive cycling step 12b) and 12c), obtain the reparation image F of each layer successivelyS-1, FS-2...,
Fk'..., F0, initiation layer repairs image F0It is final free view-point image.
The effect of the present invention can be further illustrated by following experiment:
1. simulated conditions:
CPU be Core (TM), 3.20GHZ, internal memory 4.00G, WINDOWS XP system, on Matlab R2012b platform
Emulated.
The present invention selects two groups of test images to emulate, and these two groups test image such as Fig. 3, wherein Fig. 3 (a) is Ballet
The left view point view of test set, Fig. 3 (b) is the left view point depth map of Ballet test set, and Fig. 3 (c) is Ballet test set
Right viewpoint view, Fig. 3 (d) is the right viewpoint depth map of Ballet test set；Fig. 3 (e) is a left side for Breakdancing test set
Viewpoint view, Fig. 3 (f) is the left view point depth map of Breakdancing test set, and Fig. 3 (g) is Breakdancing test set
Right viewpoint view, Fig. 3 (h) is the right viewpoint depth map of Breakdancing test set.
Emulation mode: the free view-point image combining method based on 3D conversion that 1. Y.Mori proposes
2. the free view-point image combining method filled based on background cavity that K.J.Oh proposes
3. present invention introduces the free view-point image combining method of color correction
2. emulation content:
Emulation 1, is utilized respectively above-mentioned to the Ballet test set shown in Fig. 3 (a), Fig. 3 (b), Fig. 3 (c) and Fig. 3 (d)
Three kinds of methods carry out free view-point image synthesis, result such as Fig. 4, and wherein Fig. 4 (a) is the method synthesis proposed by Y.Mori
Free view-point image, Fig. 4 (b) is the free view-point image of the method synthesis proposed by K.J.Oh, and Fig. 4 (c) is by this
The free view-point image of bright method synthesis, Fig. 4 (d) is actual reference picture.
Fig. 4 (a) and Fig. 4 (b) demonstrates that the method that Y.Mori and K.J.Oh proposes all exists obvious color and discontinuously asks
Topic, can be seen that from Fig. 4 (c) present invention efficiently solves the color discontinuous problem between dancer's the right and left and background,
And rationally it is filled with the cavity at dancer's shoulder accurately.Comparison diagram 4 (a), Fig. 4 (b), Fig. 4 (c) and Fig. 4 (d), it can be seen that
The empty filling algorithm that the present invention proposes is possible not only to effectively solve color discontinuous problem, it is also possible to filling cavity accurately,
Obtain edge clearly.
Emulation 2, to the profit respectively of the Breakdancing test set shown in Fig. 3 (e), Fig. 3 (f), Fig. 3 (g) and Fig. 3 (h)
Carrying out free view-point image synthesis, result such as Fig. 5 by above-mentioned three kinds of methods, wherein Fig. 5 (a) is the method proposed by Y.Mori
The free view-point image of synthesis, Fig. 5 (b) is the free view-point image of the method synthesis proposed by K.J.Oh, and Fig. 5 (c) is logical
Crossing the free view-point image of the inventive method synthesis, Fig. 5 (d) is actual reference picture.
Fig. 5 (a) and Fig. 4 (b) reflects that the method that Y.Mori and K.J.Oh proposes all exists a certain degree of color and do not connects
Continuous problem, can be seen that from Fig. 5 (c) present invention solves the color discontinuous problem between dancer's lower limb both sides and background, right
Ratio Fig. 5 (a), Fig. 5 (b), Fig. 5 (c) and Fig. 5 (d), it can be seen that the method that the present invention proposes solves color discontinuous problem,
Maintaining edge, experimental result is stable, is effectively increased viewing comfort level.
Claims (9)
1. introduce a free view-point image combining method for color correction, including:
1) input left and right viewpoint view and the depth map of their correspondences, based on position relationship and projection equation by left and right view and deep
Degree figure projects to intermediate-view plane, obtains left virtual view WL, right virtual view WR, left virtual depth figure DLVirtual with the right side deeply
Degree figure DR；
2) by occlusion areas W of left virtual viewL' and occlusion areas W of right virtual viewR' overlap, non-by left virtual view
Occlusion areas MLNon-blocking region M with right virtual viewROverlap；
3) by the non-blocking region M of left virtual viewLNon-blocking region M with right virtual viewRIt is weighted merging, in obtaining
Between non-blocking region part M of virtual view；
4) by the non-blocking region N of left virtual depth figureLNon-blocking region N with right virtual depth figureRIt is weighted merging,
Non-blocking region N to intermediate virtual depth map；
5) by the non-blocking region N of intermediate virtual depth map, occlusion areas D of left virtual depth figureL' and right virtual depth figure
Occlusion areas DR' merge, obtain final intermediate virtual depth map A0；
6) the non-blocking region M of middle virtual view is carried out image segmentation, isolate prospect MfWith background Mb；
7) statistics non-blocking regional background and the rectangular histogram of occlusion areas:
7a) by the background area M being partitioned intob, correspondence finds out the background area M in left virtual viewLbWith in right virtual view
Background area MRb, make intermediate virtual view background area M respectivelybStatistic histogram Hb, left virtual view background MLbStatistics
Rectangular histogram HLbWith right virtual view background MRbStatistic histogram HRb；
7b) make left virtual view occlusion areas WL' statistic histogram HL' and right virtual view occlusion areas WR' statistics Nogata
Figure HR'；
8) calculate the difference between the rectangular histogram of background area, substitute the difference between occlusion areas rectangular histogram with it, obtain centre
The statistic histogram C of occlusion areas on the left of virtual viewLStatistic histogram C with right side occlusion areasR；
9) Histogram Matching algorithm is used, by the statistic histogram H of left virtual view occlusion areasL' it is matched to intermediate virtual view
The statistic histogram C of left side occlusion areasL, obtain occlusion areas Cf on the left of the intermediate virtual view after color correctionL, in like manner will
The statistic histogram H of right virtual view occlusion areasR' it is matched to the statistic histogram C of occlusion areas on the right side of intermediate virtual viewR,
Obtain occlusion areas Cf on the right side of the intermediate virtual view after color correctionR；
10) by the non-blocking region M of intermediate virtual view, left side occlusion areas CfLWith right side occlusion areas CfRMerge,
To new intermediate virtual view B0；
11) according to virtual depth figure A0In depth information, choose background pixel to itself and intermediate virtual view B0Carry out successively
Down-sampling, obtains each layer down-sampled virtual depth figure AkWith virtual view Bk, until the virtual depth figure A of end layer S layerSAnd void
Intend view BSIn there is no cavity；
12) from the beginning of S layer, down-sampled virtual view B is the most upwards filledk'In cavity, obtain the reparation image F of each layerk',
Until obtaining initiation layer to repair image F0, the most final free view-point image.
The free view-point image combining method of introducing color correction the most according to claim 1, wherein step 3) in by a left side
The non-blocking region M of virtual viewLNon-blocking region M with right virtual viewRIt is weighted merging, carries out as follows:
3a) by distance t of left view point to intermediate-viewLDistance t with right viewpoint to intermediate-viewR, calculate virtual at synthetic mesophase
During view non-blocking region, the weight coefficient α in left virtual view non-blocking region and right virtual view non-blocking region
Weight coefficient 1-α, wherein:
3b) based on weight coefficient, it is weighted merging to left virtual view non-blocking region and right virtual view non-blocking region,
Obtain the non-blocking region part of intermediate virtual view: M=α ML+(1-α)·MR。
The free view-point image combining method of introducing color correction the most according to claim 1, wherein step 4) in by a left side
The non-blocking region N of virtual depth figureLNon-blocking region N with right virtual depth figureRIt is weighted merging, is based on step 3)
In the weight coefficient α that obtains, by the non-blocking region N of left virtual depth figureLNon-blocking region N with right virtual depth figureRCarry out
Weighting, obtains the non-blocking region part of intermediate virtual depth map: N=α NL+(1-α)·NR。
The free view-point image combining method of introducing color correction the most according to claim 1, wherein step 8) in, with the back of the body
Difference between scene area rectangular histogram substitutes the difference between occlusion areas rectangular histogram, carries out as follows:
8a) with the statistic histogram H of left virtual view backgroundLbDeduct the statistic histogram H of intermediate virtual view backgroundb, obtain
Statistical average difference value histogram Diff between left virtual view background area and intermediate virtual view background areaL；In like manner, use
The statistic histogram H of right virtual view backgroundRbDeduct the statistic histogram H of intermediate virtual view backgroundb, obtain right virtual view
Statistical average difference value histogram Diff between background area and intermediate virtual view background areaR；
8b) by statistical average difference value histogram DiffLIt is added in the statistic histogram H of left virtual view occlusion areasLOn ', obtain
The statistic histogram C of occlusion areas on the left of intermediate virtual viewL；In like manner, by statistical average difference value histogram DiffRIt is added in right void
Intend the statistic histogram H of view occlusion areasROn ', obtain the statistic histogram C of occlusion areas on the right side of intermediate virtual viewR。
The free view-point image combining method of introducing color correction the most according to claim 1, wherein step 9) in by a left side
The statistic histogram H of virtual view occlusion areasL' it is matched to the statistic histogram C of occlusion areas on the left of intermediate virtual viewL, it is
By in left virtual view occlusion areas, the pixel value projection of pixel is to left and right adjacent pixels value, inaccessible to change left virtual view
The statistic histogram H in regionL' so that it is it is equal to the statistic histogram C of occlusion areas on the left of intermediate virtual viewL。
The free view-point image combining method of introducing color correction the most according to claim 1, wherein step 9) in by the right side
The statistic histogram H of virtual view occlusion areasR' it is matched to the statistic histogram C of occlusion areas on the right side of intermediate virtual viewR, it is
By in right virtual view occlusion areas, the pixel value projection of pixel is to left and right adjacent pixels value, inaccessible to change right virtual view
The statistic histogram H in regionR' so that it is it is equal to the statistic histogram C of occlusion areas on the right side of intermediate virtual viewR。
The free view-point image combining method of introducing color correction the most according to claim 1, wherein step 11) in basis
Virtual depth figure A0In depth information, choose background pixel and it carried out successively down-sampling, be to have cavity point virtual deeply
Degree figure A0Carry out the most down-sampled process, to end layer, there is no empty point, obtain A successively0Each layer down-sampled images A1,
A2..., Ak..., AS, wherein kth layer virtual depth figure AkIt is based on its last layer virtual depth figure Ak-1Obtain, virtual depth figure
AkMiddle any point (m, n) to ask for formula as follows:
Wherein, Xm,nIt is-1 layer of virtual depth figure A of kthk-1In a size be the matrix-block of 5 × 5, its central point (2 × m+3,
2 × n+3) place；ω is the gaussian kernel of 5 × 5；Qh is used to the threshold value of division prospect and background, and depth value is the back of the body more than qh
Scape, is prospect less than qh；L (x) function is used for choosing background pixel point,In nz (u) representing matrix u non-
The number of zero point；Num (v) represents the element number meeting condition v；The value of k is incremented by S one by one by 1, and S is to make virtual depth
Figure ASIn do not have cavity point end layer.
The free view-point image combining method of introducing color correction the most according to claim 1, wherein step 11) in basis
Virtual depth figure A0In depth information, choose background pixel to virtual view B0Carry out successively down-sampling, to there being cavity point
Intermediate virtual view B0Carry out the most down-sampled process, to end layer, there is no empty point, obtain B successively0Each layer down-sampling figure
As B1, B2..., Bk..., BS, wherein arbitrarily kth layer virtual view BkIt is based on its last layer virtual view Bk-1Obtain, virtual regard
Figure BkMiddle any point (m, n) to ask for formula as follows:
Wherein, Xm,nIt is-1 layer of virtual depth figure A of kthk-1In a size be the matrix-block of 5 × 5, its central point (2 × m+3,
2 × n+3) place；Ym,nIt is-1 layer of down-sampling virtual view B of kthk-1In a size be the matrix-block of 5 × 5, its central point is (2
× m+3,2 × n+3) place；ω is the gaussian kernel of 5 × 5；Qh is used to the threshold value of division prospect and background, and depth value is more than
Qh is background, is prospect less than qh；L (x) function is used for choosing background pixel point,Nz (u) representing matrix
The number of non-zero points in u；Num (v) represents the element number meeting condition v；The value of k is incremented by S one by one by 1, S be make virtual
Depth map BSIn there is no the end layer of cavity point, be also simultaneously to make virtual depth figure ASIn do not have cavity point end layer.
The free view-point image combining method of introducing color correction the most according to claim 1, wherein step 12) in from
S layer starts, and the most upwards fills down-sampled virtual view Bk'In cavity, obtain the reparation image F of each layerk', until at the beginning of obtaining
Beginning layer repairs image F0, carry out as follows:
12a) according to the down-sampling virtual view B of S layerSIn do not have cavity characteristic, by the virtual view B of S layerSIt is equal to
The reparation image F of S layerS, i.e. FS=BS；
12b) by linear interpolation, S layer is repaired image FSUp-sample, obtain virtual with the expansion of the resolution such as S-1 layer
View ES-1, wherein ES-1In be positioned at pth row, q row point (p, pixel value E q)S-1(p, q) asks for as follows:
WhereinI', j' are used for choosing reparation image FSIn with point (p, q) centered by 3
The matrix-block of × 3, by obtaining expansion virtual view E to this matrix-block smothing filteringS-1Midpoint (p, pixel value E q)S-1(p,
Q), the value of i' here, j' is required to be even number, with satisfied reparation image FSIn pointFor effective coordinate points；
12c) with expanding virtual view ES-1In pixel, fill same layer virtual view BS-1Pixel at middle cavity,
Obtain the reparation image F of S-1 layerS-1, wherein FS-1In be positioned at pth row, q row point (p, pixel value F q)S-1(p, q) by such as
Lower formula is asked for:
12d) by step 12b), to any kth ' layer repairs image Fk'Up-sample, obtain and the resolution such as k'-1 layer
Expand virtual view Ek'-1, then by step 12c) with expanding virtual view Ek'-1In pixel fill same layer virtual view
Bk'-1Pixel at middle cavity, obtains the reparation image F of k'-1 layerk'-1, the value of k' is successively decreased to 0 one by one by S-1, i.e. by S-1
Layer starts, the most upwards repetitive cycling step 12b) and 12c), obtain the reparation image F of each layer successivelyS-1, FS-2..., Fk'...,
F0, initiation layer repairs image F0It is final free view-point image.
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