CN100584030C - Lost color vector reconstruction method and device - Google Patents
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Abstract
The invention discloses a method for reestablishing loss chromatic value component and a device therefor, which can improve definition of edge images whose chromatic value is reestablished. The invention is applied to a pixel point to be reestablished in a Bel image pixel point matrix, comprises following steps: according to acquired isochromatic value component on a first group of pixel points which surround the pixel point to be reestablished in the image pixel point matrix, calculating the smallest chromatic difference change estimated value Cv in the vertical direction and the smallest chromatic difference change estimated value Ch in the horizontal direction, wherein besides pixel points which are in the same line or the same row with the pixel point to be reestablished, the first group of pixel points also comprises other pixel points; according to the smallest chromatic difference change estimated value Cv in the vertical direction and the smallest chromatic difference change estimated value Ch in the horizontal direction, determining the second chromatic value component. Moreover, the invention also provides a device for reestablishing loss chromatic value component.
Description
Technical field
The present invention relates to a kind of lost color vector reconstruction method, finger is applied to the lost color vector reconstruction method for the treatment of on the reconstructed image vegetarian refreshments in the Bayer pattern pixel matrix especially.Also relate to a kind of lost color vector reconstruction device in addition.
Background technology
In camera market now, digital camera almost has been substituted traditional camera and has become main flow, wherein to be in traditional camera be to utilize photographic film to come sensing image to Zui Da difference, digital camera then is to utilize Charged Coupled Device (Charge Coupled Device is called for short CCD) or CMOS transducer (CMOS sensor) to come sensing image.But, because Charged Coupled Device or CMOS transducer can only sense the power of light, can not sense change in color.Therefore, when digital camera was wanted to have the ability of chromatic image sensing, the producer must add that colored filter (Color Filter is called for short CF) carries out the action of color separation in the front of Charged Coupled Device or CMOS transducer.
And colored filter all is to adopt RGB (RGB) three primary colors to carry out color separation usually, so need the pixel of three Charged Coupled Devices to capture respectively, a last pixel that again three color-values that pixel captured of three Charged Coupled Devices is mixed in the full-color image.But because the consideration on cost and the size, general digital camera only can use the pixel of single Charged Coupled Device to carry out the color acquisition, so will make each pixel can only capture a certain color-values component in RGB (RGB) three primary colors, and cause on the co-located other two color-values components to lose.
And be under low-cost and undersized restriction, to give for change because of saving the color-values component that Charged Coupled Device quantity is lost, following method for reconstructing just is developed.See also first figure, it is modal colorized optical filtering array (Color Filter Array in the present digital camera, be called for short CFA) the pattern schematic diagram, wherein each square corresponds to the pixel in the Charged Coupled Device, and by graphic know find out, green in the pattern (G), blue (B) are 2: 1: 1 with the pixel quantity ratio of red (R), and this kind arrangement mode is commonly called Bayer pattern (Bayerpattern), and its details can be with reference to No. 3971065 US Patent specification.So the color-values component that must utilize colorized optical filtering array figure shown in first figure and Charged Coupled Device to arrange in pairs or groups and capture carries out the mathematical operation of interpolative operation method again, use supposition and reconstruct other color-values that each pixel is lost.
And it is very many about the kind of interpolative operation method, but can be divided into two classes basically, the first kind is called fixed image interpolarting method, and the interpolating method that belongs to this type of has neighbor point (nearest) interpolation method, bilinearity (bilinear) interpolation method and color to change (Smooth Hue Transition) interpolation method or the like gently.Therefore but the class interpolation method is when carrying out the color-values that interpolative operation obtains to lose in a certain pixel, its neighbor point of getting is fixed with respect to the relative position of this pixel, so itself does not detect the ability at edge this type of interpolation method, so its image edge lines of rebuilding out partly can produce the phenomenon of image fog.
And be to improve above-mentioned disappearance, second class methods that are called free-standing image interpolarting method just are developed, this type of interpolation method carry out interpolative operation obtain in a certain pixel to lose fall color-values the time, its neighbor point of getting is unfixed with respect to the relative position of this pixel, meaning is the ability that this type of interpolation method itself has the detecting edge, therefore can reduce ill-defined phenomenon.For example No. 5629734 specification of United States Patent (USP) just described and utilized this type of interpolation method to carry out technological means and interlock circuit that color-values is rebuild, but the disappearance of this case technology is that it only utilizes this pixel of rebuild losing color-values with desire to go together and the color-values information of the neighborhood pixels point of same column is carried out the judgement and the selection of sampling point, cooperate shown in Figure 1, before this selected neighborhood pixels point of case all be positioned at desire rebuild to lose color-values pixel 1 the colleague and with listing (two arrow lines as shown in FIG.), so the image of rebuilding only can reduce ill-defined phenomenon limitedly.
Comprehensive above-mentioned reason can be learnt, present color-values reconstruction method still can't reflect the image edge of all directions effectively, therefore the color error after rebuilding still has sizable space of improving, and how to improve the disappearance that above-mentioned existing means go up in this respect, for developing topmost purpose of the present invention.
Summary of the invention
First technical problem to be solved by this invention provides a kind of lost color vector reconstruction method, and it can improve the definition of edge images whose chromatic after color-values is rebuild.
In addition, second technical problem to be solved by this invention provides a kind of lost color vector reconstruction device, uses the definition of edge images whose chromatic after this device can improve the color-values reconstruction.
In order to solve above first technical problem, the invention provides a kind of lost color vector reconstruction method, being applied to one in the Bayer pattern pixel matrix treats on the reconstructed image vegetarian refreshments, this treats that the reconstructed image vegetarian refreshments only has one first color-values component and loses one second color-values component and one the 3rd color-values component, this first color-values component in this pixel matrix wherein, the ratio of this second color-values component and the 3rd color-values component is 1: 2: 1, wherein, method for reconstructing comprises the following step: treat that according to surrounding this in this pixel matrix these color-values components that obtained on one first group of pixel of reconstructed image vegetarian refreshments calculate the minimum aberration of a vertical direction and change estimated value Cv and the minimum aberration variation of horizontal direction estimated value Ch, wherein in this first group of pixel except have treat with this that reconstructed image vegetarian refreshments is gone together or the pixel of same column also include other pixel; And change estimated value Cv according to the minimum aberration of this vertical direction and determine this second color-values component with the minimum aberration variation of horizontal direction estimated value Ch.
In order to solve above second technical problem, the invention provides a kind of lost color vector reconstruction device, being applied to one in the Bayer pattern pixel matrix treats on the reconstructed image vegetarian refreshments, this treats that the reconstructed image vegetarian refreshments only has one first color-values component and loses one second color-values component and one the 3rd color-values component, this first color-values component in this pixel matrix wherein, the ratio of this second color-values component and the 3rd color-values component is 1: 2: 1, wherein, and this reconstructing device comprises: a minimum aberration changes estimator, it is to read in this pixel matrix to surround this and treat that these color-values components that obtained on one first group of pixel of reconstructed image vegetarian refreshments calculate the minimum aberration of a vertical direction and change estimated value Cv and the minimum aberration variation of horizontal direction estimated value Ch, wherein in this first group of pixel except have treat with this that reconstructed image vegetarian refreshments is gone together or the pixel of same column also include other pixel; One compares determining device, signal is connected in this minimum aberration and changes estimator, it changes estimated value Cv for the minimum aberration of this vertical direction of reception and the minimum aberration of this horizontal direction changes estimated value Ch, and a Cv-Ch and a threshold value T are compared, to produce a comparison signal; And a color value arithmetic device, signal is connected in this comparison determining device, in order to produce this second color-values component in response to this comparison signal.
Because the present invention not only utilizes and treats that the reconstructed image vegetarian refreshments is gone together and the color-values information of the neighborhood pixels point of same column is carried out the judgement and the selection of sampling point, but also with reference to the color-values information of other area pixel point, so the edge blurry of reconstructed image can significantly reduce, therefore can effectively improve the disappearance of prior art.
Description of drawings
Below in conjunction with the drawings and specific embodiments the present invention is described in further detail.
Fig. 1 is the schematic diagram of modal colorized optical filtering array patterns in the present digital camera;
Fig. 2 is to be the Bayer pattern schematic diagram of 5 * 5 pixels at center with red pixel point;
Fig. 3 is to be the Bayer pattern schematic diagram of 5 * 5 pixels at center with blue pixel point;
Fig. 4 is to be the Bayer pattern schematic diagram of 5 * 5 pixels at center with green pixel point;
Fig. 5 is the steps flow chart schematic diagram that estimates out green component G13 in this case lost color vector reconstruction method;
Fig. 6 is about the function block schematic diagram of lost color vector reconstruction device in the specific embodiments of the invention.
The primary clustering symbol description.
Desire is rebuild the pixel 1 of losing color-values
Minimum aberration changes relatively determining device 41 of estimator 40
Color-values arithmetic unit 42 memory devices 43
Embodiment
See also Fig. 2, it for the purpose of following explanation is convenient, all compiles number P1～P25 with each pixel for being Bayer pattern (Bayer pattern) schematic diagram of 5 * 5 pixels at center with red pixel point.
Because center point P 13 is in actual color acquisition process, has only the color-values R13 that captures red component, and and fail to capture color-values G13, B13 green and blue component, therefore this case G13 and B13 of providing following method to reconstruct P13, and be convenience on describing, how explanation G13 rebuilds earlier.And in general situation, during loss color-values component on rebuilding the P13 point, the loss color-values component of P1 to P12 has been rebuild all and has been finished.
And main spirit of the present invention is to utilize the close theory of an aberration to calculate, its main idea is in this zone that the difference of two color-values components on certain a bit can equal the mean value of two color-values component differences on each point in this zone or the consecutive points, G-R=G '-R ' for example, G-B=G '-B ', wherein R, B, G represent the color-values component of this point, and G '-R ', G '-B ' then represents the mean value of green red and turquoise color-values component difference on each point in this zone or the consecutive points.
So use the close theory of above-mentioned aberration, define following equation:
Gv-R13＝(k1*((G8-R3)+(G18-R23))+k2*((G8-R13)+(G18-R13)))/k3；
Gh-R13＝(k1*((G12-R11)+(G14-R15))+k2*((G12-R13)+(G14-R13)))/k3；
Wherein to be defined as respectively with P13 be the green estimated value of vertical direction and the green estimated value of horizontal direction at center for Gv and Gh; Equation the right then is the mean value of green-red color-values component difference on the mean value of green-red color-values component difference and the horizontal direction consecutive points on the vertical direction consecutive points in the Bayer pattern (Bayer pattern) of 5 * 5 pixels, wherein k1, k2 and k3 are default coefficient, and R13 represents the red color value component of P13, the rest may be inferred, G8 represents the green tint value component of P8, then represents red color value component of P3 or the like as for R3.
Convenient for describing, with above-mentioned be that the mean value of green-red color-values component difference on the mean value of green-red color-values component difference on the vertical direction consecutive points at center and the horizontal direction consecutive points is represented with GRv and GRh respectively with P13, wherein
GRv＝Gv-R13＝(k1*((G8-R3)+(G18-R23))+k2*((G8-R13)+(G18-R13)))/k3；
GRh＝Gh-R13＝(k1*((G12-R11)+(G14-R15))+k2*((G12-R13)+(G14-R13)))/k3。
Then, utilize the following equation of the close theoretical definition of same aberration again:
Bv-Gv＝(k4*(B7-G12)+k5*(B17-G12)+k6*(B9-G14)+k7*(B19-G14))/k8；
Bh-Gh＝(k4*(B7-G8)+k5*(B17-G18)+k6*(B9-G8)+k7*(B19-G18))/k8；
Wherein k4, k5, k6, k7 and k8 are default coefficient, and B7 represents the blue color-values component of P7, and the rest may be inferred, and G12 represents green tint value component of P12 or the like.
In this embodiment, because R13 is known, so Gv and Gh can try to achieve respectively with GRv+R13 and GRh+R13, therefore is that the blue estimated value Bv of vertical direction and the blue estimated value Bh of horizontal direction at center also can be estimated out respectively with P13:
Bv＝Gv+(k4*(B7-G12)+k5*(B17-G12)+k6*(B9-G14)+k7*(B19-G14))/k8；
Bh＝Gh+(k4*(B7-G8)+k5*(B17-G18)+k6*(B9-G8)+k7*(B19-G18))/k8；
And be can describe convenient, with above-mentioned be that the mean value of vertical direction consecutive points blueing-green tint value component difference at center and the mean value of horizontal direction consecutive points blueing-green tint value component difference are represented with BGv and BGh respectively with P13;
BGv＝((k4*(B7-G12)+k5*(B17-G12)+k6*(B9-G14)+k7*(B19-G14))/k8)；
BGh=((k4* (B7-G8)+k5* (B17-G18)+k6* (B9-G8)+k7* (B19-G18))/k8); Conversely then
GBv＝-((k4*(B7-G12)+k5*(B17-G12)+k6*(B9-G14)+k7*(B19-G14))/k8)；
GBh＝-((k4*(B7-G8)+k5*(B17-G18)+k6*(B9-G8)+k7*(B19-G18))/k8)，
Utilize same theory to list following two formulas again and define green-red variation value GRavg and green-blue variation value GBavg on the whole on the whole in this pixel matrix;
GRavg＝Gavg-Ravg
GBavg＝Gavg-Bavg
And wherein Gavg calculates gained, the following formula of definable from the green tint value component that with P13 is at least 18 pixels 5 * 5 pixels at center (be not limited to known or forecasting institute gets):
Gavg=(a1* (G8+G12+G14+G18)+a2* (G2+G4+G6+G10+G16+G20+G22+G24)+a3* (G1+G3+G5+G7+G9+G11))/a4; Wherein a1, a2, a3 and a4 are default coefficient.
Ravg calculates gained, the following formula of definable from the red color value component that with P13 is at least 14 pixels 5 * 5 pixels at center (be not limited to known or forecasting institute gets):
Ravg=(a5*R13+a6* (R1+R5+R21+R25)+a7* (R3+R11+R15+R23)+a8* (R2+R6)+a9* (R4+R8+R12))/a10; Wherein a5, a6, a7, a8, a9 and a10 are default coefficient.
As for Bavg then is to calculate gained, the following formula of definable from the blue color-values component that is at least 12 pixels 5 * 5 pixels at center (be not limited to known or forecasting institute gets) with P13:
Bavg=(a11* (B7+B9+B17+B19)+a12* (B2+B4+B6+B10)+a13* (B3+B8)+a14* (B1+B5))/a15; Wherein a11, a12, a13, a14 and a15 are default coefficient.
And after obtaining above-mentioned data, it is the minimum aberration variation estimated value Cv of vertical direction and the minimum aberration variation of the horizontal direction estimated value Ch at center that present embodiment utilizes following formula to obtain with P13 again, wherein
Cv＝e1*|GRv-GRavg|+e2*|GBv-GBavg|+e3*|R3-R13|+e4*|R23-R13|+e5*|G18-G8|+e6*|G7-G12|+e7*|G9-G14|
Ch=e1*|GRh-GRavg|+e2*|GBh-GBavg|+e3*|R11-R13|+e4*|R15-R1 3|+e5*|G14-G12|+e6*|G7-G8|+e7*|G9-G8|; Wherein e1, e2, e3, e4, e5, e6 and e7 are default coefficient.
It is the center that minimum aberration variation estimated value Cv of vertical direction and the minimum aberration variation of horizontal direction estimated value Ch represent with this pixel P13 respectively, respectively vertically with the color change degree of horizontal direction, the color change degree of big more this direction of representative of estimated value is big more, otherwise, then represent the color change degree of this direction smaller when estimated value is more little.
In view of the above, in present embodiment, this pixel P13 goes up the green tint value component G13 of leakage originally, and available following formula pushes away:
(1) if Cv-Ch＜T represents vertical direction color change degree smaller, then G13=first estimated value=Gvs.
(2) if Cv-Ch＞T represents horizontal direction color change degree smaller, then G13=second estimated value=Ghs.
(3) if Cv-Ch=T represents vertical direction identical with horizontal direction color change degree, G13=the 3rd estimated value=(G8+G12+G18+G14)/4 then.
Wherein T is a predetermined threshold value, and the first estimated value Gvs and the second estimated value Ghs then push away with following formula:
Gvs＝s1*R13+s2*(s3*((G8-R3)+(G18-R23))+s4*((G8-R13)+(G18-R13)))/s5+s6*Bv+s7*GBavg；
Wherein:
s2*(s3*((G8-R3)+(G18-R23))+s4*((G8-R13)+(G18-R13)))/s5
Representative treats that with this reconstructed image vegetarian refreshments P13 is center and the mean value of representing in this pixel matrix this second color-values component and this first color-values component difference on the vertical direction consecutive points.
Ghs＝s1*R13+s2*(s3*((G12-R11)+(G14-R15))+s4*((G12-R13)+(G14-R13)))/s5+s6*Bh+s7*GBavg；
Wherein:
s2*(s3*((G12-R11)+(G14-R15))+s4*((G12-R13)+(G14-R13)))/s5
Representative treats that with this reconstructed image vegetarian refreshments P13 is center and the mean value of representing in this pixel matrix this second color-values component and this first color-values component difference on the horizontal direction consecutive points, is default coefficient as for s1, s2, s3, s4, s5, s6 and s7.
After obtaining G13, pixel P13 goes up the blue color-values component B13 of leakage originally, then can utilize with P13 is that at least 17 color-values components in 5 * 5 pixels at center (be not limited to known or forecasting institute gets) calculate gained, and its algorithm can be as following formula:
B13=G13+ (f1* ((B7-G7)+(B9-G9)+(B17-G17)+(B19-G19))+f2* ((B8-G8)+(B14-G14)+(B18-G18)+(B12-G12)))/f3; Wherein f1, f2 and f3 are default coefficient.
In sum, be the Bayer pattern (Bayerpattern) of 5 * 5 pixels at center with red pixel point R13, can utilize said method to obtain the G13 and the B13 of loss.In like manner as can be known, as shown in Figure 3 be the Bayer pattern (Bayerpattern) of 5 * 5 pixels at center with blue pixel point B13, because blue pixel point B13 and red pixel point R13 are all and respectively account for 25 percent ratio, also can utilize above-mentioned G13 and the R13 that obtains loss with quadrat method.
As for shown in Figure 4 be the Bayer pattern (Bayer pattern) of 5 * 5 pixels at center with green pixel point G13, it is that the color-values component (be not limited to known or forecasting institute gets) of at least 9 pixels in 5 * 5 pixels at center calculates gained that the R13 of its loss and B13 then can utilize with G13, and its algorithm can be as following formula:
R13＝G13+(w1*((R12-G12)+(R14-G14))+w2*((R8-G8)+(R18-G18)))/w3
B13＝G13+(w1*((B8-G8)+(B18-G18))+w2*((B12-G12)+(B14-G14)))/w3；
Wherein w1, w2 and w3 are default coefficient.
And above-mentioned coefficient: k1～k8, a1～a15, e1～e7, s1～s7, f1～f3 and the w1～w3 of respectively organizing, it can utilize several reference patterns to carry out emulation (simulation) and calculate, come computing to approach out one group of preferable coefficient to minimize root-mean-square error, minimize root-mean-square error and can be the personage who knows this skill and understand, do not repeat them here.
See also Fig. 5 in addition again, it is for estimating out the steps flow chart schematic diagram of green component G13 in the above-mentioned lost color vector reconstruction method, at first, treat that according to surrounding this in this pixel matrix these color-values components that obtained on first group of pixel of reconstructed image vegetarian refreshments P13 calculate the minimum aberration of vertical direction and change estimated value Cv and the minimum aberration variation of horizontal direction estimated value Ch, wherein treat with this that reconstructed image vegetarian refreshments is gone together or the pixel of same column (P3 for example except having in this first group of pixel, P23, P8, P18, P11, P15 etc.) outer other pixel (P7 that also includes, P12, P9, P14, P8 etc.); Utilize minimum aberration variation estimated value Cv of vertical direction and the minimum aberration of horizontal direction to change estimated value Ch then and carry out following judgement:
(1) if Cv-Ch＜T represents vertical direction color change degree smaller, then G13=first estimated value=Gvs;
(2) if Cv-Ch＞T represents horizontal direction color change degree smaller, then G13=second estimated value=Ghs; And
(3) if Cv-Ch=T represents vertical direction identical with horizontal direction color change degree, G13=the 3rd estimated value=(G8+G12+G18+G14)/4 then.
Wherein this first estimated value Gvs treats that according to surrounding this in this pixel matrix these color-values components and the close theory of aberration that have obtained on one second group of pixel of reconstructed image vegetarian refreshments calculate, and wherein treats also to include other pixel (P7, P12, P9, P14, P17, P19 etc.) the pixel (for example P3, P23, P8, P18, P13 etc.) that the reconstructed image vegetarian refreshments goes together except having with this in this second group of pixel.
And this second estimated value Ghs treats that according to surrounding this in this pixel matrix these color-values components and the close theory of aberration that have obtained on one the 3rd group of pixel of reconstructed image vegetarian refreshments calculate, and wherein treats also to include other pixel (for example P7, P8, P9, P17, P18, P19 etc.) the pixel (for example P11, P12, P13, P14, P15 etc.) of reconstructed image vegetarian refreshments same column except having with this in the 3rd group of pixel.
Include in this pixel matrix around this as for the 3rd estimated value and to treat green tint value component average on one the 4th group of pixel (for example P8, P12, P18, P14) of reconstructed image vegetarian refreshments.
Fig. 6 is about the function block schematic diagram of lost color vector reconstruction device in the specific embodiments of the invention, it consists predominantly of minimum aberration and changes estimator 40, compare determining device 41, color-values arithmetic unit 42 and memory device 43 (memory device is memory device just), and wherein these memory device 43 signals are connected in this minimum aberration variation estimator 40 and this color-values arithmetic unit 42, mainly be Bayer pattern (Bayerpattern) pixel matrix, and provide this minimum aberration variation estimator and this color-values arithmetic unit to read and write in order to 5 * 5 pixels of storage shown in second figure.And minimum aberration changes estimator 40 and reads and surround this in this stored pixel matrix of memory device 43 and treat that these color-values components that obtained on this first group of pixel of reconstructed image vegetarian refreshments calculate the minimum aberration of this vertical direction and change estimated value Cv and the minimum aberration variation of this horizontal direction estimated value Ch.And signal is connected in the comparison determining device 41 that this minimum aberration changes estimator 40, it is to receive the minimum aberration of this vertical direction to change estimated value Cv and the minimum aberration variation of this horizontal direction estimated value Ch, and a Cv-Ch and a threshold value T compared, when Cv-Ch sends one first signal during less than this threshold value T, when Cv-Ch sends a secondary signal during greater than this threshold value T; And when equaling this threshold value T, sends Cv-Ch one the 3rd signal.At last, signal is connected in this comparison determining device 41 and the color-values arithmetic unit 42 that reads memory device 43, it is to read in response to the triggering of this first signal in the memory device 43 to surround this in this pixel matrix and treat that these color-values components that obtained on one second group of pixel of reconstructed image vegetarian refreshments and the close theory of aberration calculate one first estimated value and come to treat this second color-values component of reconstructed image vegetarian refreshments and write in the memory device 43 as this, read in response to the triggering of this secondary signal in addition in the memory device 43 and to surround this in this pixel matrix and treat that these color-values components that obtained on one the 3rd group of pixel of reconstructed image vegetarian refreshments and the close theory of aberration calculate one second estimated value and treat this second color-values component of reconstructed image vegetarian refreshments and write memory device 43 as this, wherein treat also to include other pixel the pixel that the reconstructed image vegetarian refreshments goes together except having in this second group of pixel, and treat also to include other pixel the pixel of reconstructed image vegetarian refreshments same column except having in the 3rd group of pixel with this with this.In sum, this case technology is not only utilized and is treated that the reconstructed image vegetarian refreshments is gone together and the color-values information of the neighborhood pixels point of same column is carried out the judgement and the selection of sampling point, but also with reference to the color-values information of other area pixel point, so the edge blurry of reconstructed image more can significantly reduce, therefore effectively improve existing means disappearance, and then reach development main purpose of the present invention; All other do not break away from the equivalence of being finished under the disclosed spirit and changes or modification, all should be included in the claim.
Claims (11)
1, a kind of lost color vector reconstruction method, being applied to one in the Bayer pattern pixel matrix treats on the reconstructed image vegetarian refreshments, this treats that the reconstructed image vegetarian refreshments only has one first color-values component and loses one second color-values component and one the 3rd color-values component, wherein the ratio of this first color-values component, this second color-values component and the 3rd color-values component is 1: 2: 1 in this pixel matrix, it is characterized in that method for reconstructing comprises the following step:
Treat that according to surrounding this in this pixel matrix the color-values component that has obtained on one first group of pixel of reconstructed image vegetarian refreshments calculates the minimum aberration of a vertical direction and changes estimated value Cv and the minimum aberration variation of horizontal direction estimated value Ch, wherein in this first group of pixel except have treat with this that reconstructed image vegetarian refreshments is gone together or the pixel of same column also include other pixel; And
Determine this second color-values component according to minimum aberration variation estimated value Cv of this vertical direction and the minimum aberration variation of horizontal direction estimated value Ch.
2, lost color vector reconstruction method as claimed in claim 1, it is characterized in that, wherein changing estimated value Cv according to the minimum aberration of this vertical direction determines the step of this second color-values component to comprise with the minimum aberration variation of horizontal direction estimated value Ch: when Cv-Ch less than a threshold value T, this this second color-values component for the treatment of the reconstructed image vegetarian refreshments just is one first estimated value, and this first estimated value treats that according to surrounding this in this pixel matrix the color-values component and the close theory of aberration that have obtained on one second group of pixel of reconstructed image vegetarian refreshments calculate, and wherein treats also to include other pixel the pixel that the reconstructed image vegetarian refreshments goes together except having with this in this second group of pixel.
3, lost color vector reconstruction method as claimed in claim 1, it is characterized in that, wherein changing estimated value Cv according to the minimum aberration of this vertical direction determines the step of this second color-values component to comprise with the minimum aberration variation of horizontal direction estimated value Ch: when Cv-Ch greater than this threshold value T, this this second color-values component for the treatment of the reconstructed image vegetarian refreshments just is one second estimated value, and this second estimated value treats that according to surrounding this in this pixel matrix the color-values component and the close theory of aberration that have obtained on one the 3rd group of pixel of reconstructed image vegetarian refreshments calculate, and wherein treats also to include other pixel the pixel of reconstructed image vegetarian refreshments same column except having with this in the 3rd group of pixel.
4, lost color vector reconstruction method as claimed in claim 1 is characterized in that, wherein Cv has the composition of following numerical value:
Treat that with this reconstructed image vegetarian refreshments is center and the mean value of representing in this pixel matrix this second color-values component and this first color-values component difference on the vertical direction consecutive points, and treat that with this reconstructed image vegetarian refreshments is the absolute value between center and the mean value of representing in this pixel matrix this second color-values component and this first color-values component difference on the whole;
Treat that with this reconstructed image vegetarian refreshments is center and the mean value of representing in this pixel matrix this second color-values component and the 3rd color-values component difference on the vertical direction consecutive points, and treat that with this reconstructed image vegetarian refreshments is the absolute value between center and the mean value of representing in this pixel matrix this second color-values component and the 3rd color-values component difference on the whole;
Treat that with this reconstructed image vegetarian refreshments is the center and represents in this pixel matrix on the vertical axis this first color-values component difference on two pixels; And
Treat that with this reconstructed image vegetarian refreshments is the center and represents in this pixel matrix on the vertical direction of vertical axis both sides this second color-values component difference on two pixels.
5, lost color vector reconstruction method as claimed in claim 1 is characterized in that, wherein the minimum aberration of this horizontal direction changes the composition that estimated value Ch has following numerical value:
Treat that with this reconstructed image vegetarian refreshments is center and the mean value of representing in this pixel matrix this second color-values component and this first color-values component difference on the horizontal direction consecutive points, and treat that with this reconstructed image vegetarian refreshments is the absolute value between center and the mean value of representing in this pixel matrix this second color-values component and this first color-values component difference on the whole;
Treat that with this reconstructed image vegetarian refreshments is center and the mean value of representing in this pixel matrix this second color-values component and the 3rd color-values component difference on the horizontal direction consecutive points, and treat that with this reconstructed image vegetarian refreshments is the composition of the absolute value between center and the mean value of representing in this pixel matrix this second color-values component and the 3rd color-values component difference on the whole;
Treat that with this reconstructed image vegetarian refreshments is the center and represents in this pixel matrix on the trunnion axis this first color-values component difference on two pixels; And
Treat that with this reconstructed image vegetarian refreshments is the center and represents in this pixel matrix on the horizontal direction of trunnion axis both sides this second color-values component difference on two pixels.
6, lost color vector reconstruction method as claimed in claim 2, it is characterized in that wherein this first estimated value includes this first color-values component for the treatment of the reconstructed image vegetarian refreshments with this, treat that with this reconstructed image vegetarian refreshments is center and the mean value of representing in this pixel matrix this second color-values component and this first color-values component difference on the vertical direction consecutive points, treat that with this reconstructed image vegetarian refreshments is the center and represents in this pixel matrix the 3rd color-values component estimated value on the vertical direction, treat that with this reconstructed image vegetarian refreshments is the center and represents in this pixel matrix the composition of the mean value of this second color-values component and the 3rd color-values component difference on the whole.
7, lost color vector reconstruction method as claimed in claim 3, it is characterized in that wherein this second estimated value includes this first color-values component for the treatment of the reconstructed image vegetarian refreshments with this, treat that with this reconstructed image vegetarian refreshments is center and the mean value of representing in this pixel matrix this second color-values component and this first color-values component difference on the horizontal direction consecutive points, treat that with this reconstructed image vegetarian refreshments is the center and represents in this pixel matrix the 3rd color-values component estimated value on the horizontal direction and treat that with this reconstructed image vegetarian refreshments is the center and represents in this pixel matrix the composition of the mean value of this second color-values component and the 3rd color-values component difference on the whole.
8, lost color vector reconstruction method as claimed in claim 1, it is characterized in that, wherein determine the step of this second color-values component to comprise: when Cv-Ch equals this threshold value T according to minimum aberration variation estimated value Cv of this vertical direction and the minimum aberration variation of horizontal direction estimated value Ch, this this second color-values component for the treatment of the reconstructed image vegetarian refreshments just is one the 3rd estimated value, and the 3rd estimated value includes in this pixel matrix around this treats the second color-values component average on one the 4th group of pixel of reconstructed image vegetarian refreshments.
9, lost color vector reconstruction method as claimed in claim 1, it is characterized in that, wherein also comprise the following step: after obtaining this this second color-values component for the treatment of the reconstructed image vegetarian refreshments, convenient treat this second color-values component of reconstructed image vegetarian refreshments and treat on average calculating of the 3rd color-values component and this second color-values component difference on eight pixels around the reconstructed image vegetarian refreshments, and then draw the 3rd color-values component that this treats the reconstructed image vegetarian refreshments around this with this.
10, a kind of lost color vector reconstruction device, being applied to one in the Bayer pattern pixel matrix treats on the reconstructed image vegetarian refreshments, this treats that the reconstructed image vegetarian refreshments only has one first color-values component and loses one second color-values component and one the 3rd color-values component, wherein the ratio of this first color-values component, this second color-values component and the 3rd color-values component is 1: 2: 1 in this pixel matrix, it is characterized in that this reconstructing device comprises:
One minimum aberration changes estimator, it is to read in this pixel matrix to surround this and treat that the color-values component that has obtained on one first group of pixel of reconstructed image vegetarian refreshments calculates the minimum aberration of a vertical direction and changes estimated value Cv and the minimum aberration variation of horizontal direction estimated value Ch, wherein in this first group of pixel except have treat with this that reconstructed image vegetarian refreshments is gone together or the pixel of same column also include other pixel;
One compares determining device, signal is connected in this minimum aberration and changes estimator, it changes estimated value Cv for the minimum aberration of this vertical direction of reception and the minimum aberration of this horizontal direction changes estimated value Ch, and a Cv-Ch and a threshold value T are compared, to produce a comparison signal; And
One color value arithmetic device, signal are connected in this comparison determining device, in order to be produced this second color-values component by comparison signal.
11, lost color vector reconstruction device as claimed in claim 10, it is characterized in that, wherein relatively determining device sends one first signal in this comparison signal during less than this threshold value T as Cv-Ch, and send a secondary signal in this comparison signal during greater than this threshold value T as Cv-Ch, and this color-values arithmetic unit is for treating that according to surrounding this in this pixel matrix the color-values component that obtained on one second group of pixel of reconstructed image vegetarian refreshments and the close theory of aberration calculate one first estimated value and treat this second color-values component of reconstructed image vegetarian refreshments as this by the triggering of first signal, treat that according to surrounding this in this pixel matrix the color-values component that obtained on one the 3rd group of pixel of reconstructed image vegetarian refreshments and the close theory of aberration calculate one second estimated value and treat this second color-values component of reconstructed image vegetarian refreshments as this by the triggering of secondary signal in addition, wherein treat also to include other pixel the pixel that the reconstructed image vegetarian refreshments goes together except having in this second group of pixel, and treat also to include other pixel the pixel of reconstructed image vegetarian refreshments same column except having in the 3rd group of pixel with this with this.
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