CN105915757B - A kind of color calibration method based on RGB color channel error function - Google Patents

A kind of color calibration method based on RGB color channel error function Download PDF

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CN105915757B
CN105915757B CN201610392971.4A CN201610392971A CN105915757B CN 105915757 B CN105915757 B CN 105915757B CN 201610392971 A CN201610392971 A CN 201610392971A CN 105915757 B CN105915757 B CN 105915757B
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CN105915757A (en
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薛远洋
李岑
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Beijing Tuoling Xinsheng Technology Co.,Ltd.
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Beijing Tuoling Inc
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
    • H04N1/46—Colour picture communication systems
    • H04N1/56—Processing of colour picture signals
    • H04N1/60—Colour correction or control
    • H04N1/6075—Corrections to the hue
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformation in the plane of the image
    • G06T3/40—Scaling the whole image or part thereof
    • G06T3/4038—Scaling the whole image or part thereof for image mosaicing, i.e. plane images composed of plane sub-images

Abstract

The invention discloses a kind of color calibration methods based on RGB color channel error function, belong to technical field of virtual reality, which comprises the following steps：Step 1: calculate color average of all images in the rgb space of overlapping region using the overlapping region between the image and image of input；Step 2: the color average generated using error function and step 1, calculates the color gain between each image；Step 3: correction of color gain；Step 4: iterative calculation color gain；Step 5: calculate final color gain；Step 6: the final color gain generated using step 5 corrects all images.The color calibration method is used to be corrected the solid colour the multiple image with overlapping region, the exposure gain accuracy of calculating is high, make the color of image difference after correction small, splice gap unobvious, and it is not high the overlapping region proportion requirement image.

Description

A kind of color calibration method based on RGB color channel error function
Technical field
The present invention relates to technical field of virtual reality, and in particular to a kind of color based on RGB color channel error function Bearing calibration, for having the solid colour correction between the multiple image of overlapping region.
Background technology
Recently as the fast development of virtual reality technology, civilian consumer level products come into domestic market, wherein The panoramic mosaic technology implementation method most directly perceived, most economical as field of virtual reality has obtained widely in multiple fields Using, such as：Street view service, panoramic map, panoramic video etc..Panoramic mosaic is the figure of the multiple and different visual angle camera shootings of suture Picture forms a continuous, seamless significantly image, and the solid colour correction wherein between multiple image is panoramic mosaic algorithm Important technology.
By being found to the retrieval of prior art literature, the paper that Matthew and David in 2006 is delivered at it 《Automatic Panoramic Image Stitching Using Invariant Features》It is middle to propose to utilize error Function carrys out the difference in exposure between correction chart picture.Yingen and Kari is in article within 2010《Color Matching of Image Sequences with Combined Gamma and Linear Corrections》In error function is improved, RGB color is replaced with reference to gamma and linear bearing calibration, while with YCbCr color spaces, has obtained preferable color Correct result.
But the prior art is not when still addressing how to make panoramic mosaic between multiple image the problem of solid colour, it is existing The correction accuracy of color correction algorithm in technology is relatively low, can not realize automatically correcting, it is necessary to intervene adjustment manually for color.
The content of the invention
It is an object of the invention to provide a kind of color calibration method based on RGB color channel error function, to solve The problem of color is inconsistent between multiple image during panoramic mosaic improves color correction precision, realizes automatically correcting for color, reduces Intervene adjustment manually.
To achieve the above object, the color calibration method of the present invention based on RGB color channel error function includes Following steps：
Step 1: it is empty to calculate RGB of all images in overlapping region using the overlapping region between the image and image of input Between color average；
Step 2: the color average generated using error function and step 1, calculates the color gain between each image；
Step 3: correction of color gain；
Step 4: iterative calculation color gain；
Step 5: calculate final color gain；
Step 6: the final color gain generated using step 5 corrects all images.
All images described in step 1 are calculated in the color average of the rgb space of overlapping region according to the first formula Go out, the first formula is：
The wherein overlapping region of R (i, j) representative image i and image j,Pixel in representative image i is respectively in RGB The color value of passage,Representative image i is in the color average of the overlapping region with image j.
The color gain between each image described in step 2 is calculated according to the second formula, and the second formula is：
The wherein quantity of n representative images,WithThe color of each comfortable RGB channels of representative image i and image j increases respectively Benefit；NijRepresentative image i and image j is in the pixel quantity of overlapping region；Parameter δNAnd δgColor error and gain are represented respectively Standard deviation；Go out for null solution the color gain g in equation by the derivative of step-up error function.
Color error δ described in step 2N10 are arranged to, the standard deviation δ of gaingIt is arranged to 0.1.
The process of correction of color gain described in step 3 is：
According to the 3rd formula, by the current color gain of each imageDivided by the average value of whole gainsAs Color gain after correctionWherein the 3rd formula is：
The average value of whole gainsIt can be calculated by the 4th formula, the 4th formula is：
The wherein quantity of n representative images.
The process of iterative calculation color gain described in step 4 is：
Judge the color gain after the correction of each comfortable RGB channel of all images before iteration according to the 5th formula firstWhether the condition of convergence is met, and the 5th formula is：
Wherein t represents current iterations, and ε is experience setting value；
If color gain of any image after the correction of any Color ChannelIt does not meet represented by the 5th formula The condition of convergence, then utilize color gainCorrection image i according to caused by above-mentioned first formula is gone according to the 6th formula Color average6th formula is：
WhereinRepresent the color average after the correction that current iteration number is t,Represent that iterations is Color average after the correction of t-1；
Then step 2 is repeated, while records the color gain that each iteration generatesFor generating final color Gain.
ε in 5th formula is rule of thumb arranged to 0.0001.
The process of the final color gain of calculating described in step 5 is：
Terminate iterative cycles when meeting the condition of convergence represented by the 5th formula, according to the 7th formula, using every time repeatedly For the color gain of cycle calculationsGenerate final color gain7th formula is：
Wherein T represents whole iterationses.
The process of the final color gain correction all images generated using step 5 described in step 6 is：According to 8th formula correction all images, the 8th formula are：
Wherein viWhole pixels of representative image i,Final color gain is represented,The color of representative image i Average,Represent final color average.
The invention has the advantages that：Color calibration method of the present invention based on RGB color channel error function Compared with prior art, the exposure gain accuracy of calculating is high, makes the color of image difference after correction small, and splicing gap is unknown It is aobvious and not high the overlapping region proportion requirement image.
Description of the drawings
Fig. 1 is the FB(flow block) of the color calibration method of the present invention based on RGB color channel error function.
Specific embodiment
Following embodiment is not limited to the scope of the present invention for illustrating the present invention.
As shown in Figure 1, the color calibration method of the present invention based on RGB color channel error function includes following step Suddenly：
Step 1: it is empty to calculate RGB of all images in overlapping region using the overlapping region between the image and image of input Between color averageSuch as the first formula (1) is shown, wherein the overlapping region of R (i, j) representative image i and image j. Pixel in representative image i respectively in the color value of RGB channel,Representative image i is in the color of the overlapping region with image j Average.
Denominator in formula (1) represents whole pixel quantities in the R of overlapping region, such as in the R of overlapping region altogether There are 100 pixels, denominator is meant that the summation of 100 1, as a result equal to 100.
Step 2: the color average generated using error function and step 1, calculates the color gain between each image, such as Second formula (2) is shown, wherein the quantity of n representative images,WithEach comfortable RGB channels of representative image i and image j respectively Color gain.NijRepresentative image i and image j is in the pixel quantity of overlapping region.Parameter δNAnd δgColor error is represented respectively With the standard deviation of gain, 10 and 0.1 are respectively set to.Go out the side of the second formula for null solution by the derivative of step-up error function Color gain g in journey.
Step 3: correction of color gain；
The correction of color beneficiating process is：
In order to which the average value for ensureing the color gain g of each all images is similar to 1, it is necessary to current face by each image Color gainDivided by the average value of whole gainsAs the color gain after correctionAs shown in the 3rd formula (3), The average value of wherein whole gainsIt can be calculated by the 4th formula (4).
Step 4: iterative calculation color gain；
The iterative calculation color gain process is：
In order to improve the accuracy of correction, the color error of the image i after correction and image j is made to be similar to 0, it is necessary to iteration Ground repeats step 2, and color gain g is recalculated using the color average newly corrected.Therefore all figures are first determined whether before iteration As each comfortable RGB channel correction after color gainWhether the condition of convergence is met, such as shown in the 5th formula (5), wherein T represents current iterations, and ε is arranged to 0.0001 by experience.
If color gain of any image after the correction of any Color ChannelThe condition of convergence is not met, then Utilize color gainGo the color average of image i caused by correction above-mentioned formula (1)Such as the 6th formula (6) institute Show, whereinRepresent the color average after the correction that current iteration number is t,It is t-1's to represent iterations Color average after correction the purpose for the arrangement is that instead of the color correction of entire image, reduces calculation amount and improves computational efficiency. Then repeat step 2, while and record the color gain that each iteration generatesFor generating final color gain.
Step 5: calculate final color gain；
The final color gain process of described calculating is：
Terminate iterative cycles when meeting the condition of convergence, the color gain that each iterative cycles is utilized to calculateGeneration Final color gainAs shown in the 7th formula (7), wherein T represents whole iterationses.
Step 6: the final color gain generated using step 5 corrects all images, such as shown in the 8th formula (8), Wherein viWhole pixels of representative image i,Final color gain is represented,The color average of representative image i,Represent final color average.
Although above having used general explanation and specific embodiment, the present invention is described in detail, at this On the basis of invention, it can be made some modifications or improvements, this will be apparent to those skilled in the art.Therefore, These modifications or improvements without departing from theon the basis of the spirit of the present invention belong to the scope of protection of present invention.

Claims (5)

  1. A kind of 1. color calibration method based on RGB color channel error function, which is characterized in that the color calibration method Comprise the following steps：
    Step 1: calculate rgb space of all images in overlapping region using the overlapping region between the image and image of input Color average；
    Step 2: the color average generated using error function and step 1, calculates the color gain between each image；
    Step 3: correction of color gain；
    Step 4: iterative calculation color gain；
    Step 5: calculate final color gain；
    Step 6: the final color gain generated using step 5 corrects all images；
    Wherein, all images described in step 1 are calculated in the color average of the rgb space of overlapping region according to the first formula It draws, the first formula is：
    <mrow> <msubsup> <mover> <mi>I</mi> <mo>&amp;OverBar;</mo> </mover> <mrow> <mi>i</mi> <mi>j</mi> </mrow> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> </msubsup> <mo>=</mo> <mfrac> <mrow> <msub> <mi>&amp;Sigma;</mi> <mrow> <msub> <mi>u</mi> <mi>i</mi> </msub> <mo>&amp;Element;</mo> <mi>R</mi> <mrow> <mo>(</mo> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> <mo>)</mo> </mrow> </mrow> </msub> <msubsup> <mi>I</mi> <mi>i</mi> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> </msubsup> <mrow> <mo>(</mo> <msub> <mi>u</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> </mrow> <mrow> <msub> <mi>&amp;Sigma;</mi> <mrow> <msub> <mi>u</mi> <mi>i</mi> </msub> <mo>&amp;Element;</mo> <mi>R</mi> <mrow> <mo>(</mo> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> <mo>)</mo> </mrow> </mrow> </msub> <mn>1</mn> </mrow> </mfrac> <mo>,</mo> <mi>c</mi> <mo>&amp;Element;</mo> <mrow> <mo>{</mo> <mrow> <mi>R</mi> <mo>,</mo> <mi>G</mi> <mo>,</mo> <mi>B</mi> </mrow> <mo>}</mo> </mrow> <mo>;</mo> </mrow>
    The wherein overlapping region of R (i, j) representative image i and image j,Pixel in representative image i is respectively in RGB channel Color value,Representative image i is in the color average of the overlapping region with image j；
    Wherein, the color gain between each image described in step 2 is calculated according to the second formula, and the second formula is：
    <mrow> <mi>g</mi> <mo>=</mo> <mfrac> <mn>1</mn> <mn>2</mn> </mfrac> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>N</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mrow> <mo>(</mo> <msup> <mrow> <mo>(</mo> <mrow> <msubsup> <mi>g</mi> <mi>i</mi> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> </msubsup> <msubsup> <mover> <mi>I</mi> <mo>&amp;OverBar;</mo> </mover> <mrow> <mi>i</mi> <mi>j</mi> </mrow> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> </msubsup> <mo>-</mo> <msubsup> <mi>g</mi> <mi>j</mi> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> </msubsup> <msubsup> <mover> <mi>I</mi> <mo>&amp;OverBar;</mo> </mover> <mrow> <mi>j</mi> <mi>i</mi> </mrow> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> </msubsup> </mrow> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>/</mo> <msubsup> <mi>&amp;delta;</mi> <mi>N</mi> <mn>2</mn> </msubsup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <mrow> <mn>1</mn> <mo>-</mo> <msubsup> <mi>g</mi> <mi>i</mi> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> </msubsup> </mrow> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>/</mo> <msubsup> <mi>&amp;delta;</mi> <mi>g</mi> <mn>2</mn> </msubsup> <mo>)</mo> </mrow> </mrow>
    The wherein quantity of n representative images,WithThe color gain of each comfortable RGB channels of representative image i and image j respectively； NijRepresentative image i and image j is in the pixel quantity of overlapping region；Parameter δNAnd δgThe standard of color error and gain is represented respectively Deviation；Go out for null solution the color gain g in the second formula equation by the derivative of step-up error function；
    Wherein, the process of the correction of color gain described in step 3 is：
    According to the 3rd formula, by the current color gain of each imageDivided by the average value of whole gainsAs correction Color gain afterwardsWherein the 3rd formula is：
    <mrow> <msubsup> <mi>g</mi> <mi>i</mi> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <msubsup> <mi>g</mi> <mi>i</mi> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> </msubsup> <msup> <mover> <mi>g</mi> <mo>&amp;OverBar;</mo> </mover> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> </msup> </mfrac> </mrow>
    The average value of whole gainsIt can be calculated by the 4th formula, the 4th formula is：
    <mrow> <msup> <mover> <mi>g</mi> <mo>&amp;OverBar;</mo> </mover> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> </msup> <mo>=</mo> <mfrac> <mrow> <msub> <mi>&amp;Sigma;</mi> <mrow> <mi>i</mi> <mo>&amp;Element;</mo> <mi>n</mi> </mrow> </msub> <msubsup> <mi>g</mi> <mi>i</mi> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> </msubsup> </mrow> <mi>n</mi> </mfrac> <mo>;</mo> </mrow>
    The wherein quantity of n representative images；
    Wherein, the process of the iterative calculation color gain described in step 4 is：
    Judge the color gain after the correction of each comfortable RGB channel of all images before iteration according to the 5th formula first Whether the condition of convergence is met, and the 5th formula is：
    <mrow> <mo>|</mo> <msubsup> <mi>g</mi> <mi>i</mi> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>-</mo> <mn>1</mn> <mo>|</mo> <mo>&lt;</mo> <mi>&amp;epsiv;</mi> </mrow>
    Wherein t represents current iterations, and ε is experience setting value；
    If color gain of any image after the correction of any Color ChannelThe receipts represented by the 5th formula are not met Condition is held back, then utilizes color gainThe face of correction image i according to caused by above-mentioned first formula is gone according to the 6th formula Color average6th formula is：
    <mrow> <msubsup> <mover> <mi>I</mi> <mo>&amp;OverBar;</mo> </mover> <mrow> <mi>i</mi> <mi>j</mi> </mrow> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>=</mo> <msubsup> <mover> <mi>I</mi> <mo>&amp;OverBar;</mo> </mover> <mrow> <mi>i</mi> <mi>j</mi> </mrow> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>i</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> <mo>&amp;times;</mo> <msubsup> <mi>g</mi> <mi>i</mi> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </mrow>
    WhereinRepresent the color average after the correction that current iteration number is t,It is t-1's to represent iterations Color average after correction；
    Then step 2 is repeated, while records the color gain that each iteration generatesIncrease for generating final color Benefit.
  2. 2. color calibration method as described in claim 1, which is characterized in that the color error δ described in step 2NIt is arranged to 10, the standard deviation δ of gaingIt is arranged to 0.1.
  3. 3. color calibration method as described in claim 1, which is characterized in that the ε in the 5th formula is rule of thumb arranged to 0.0001。
  4. 4. color calibration method as described in claim 1, which is characterized in that the final color that calculates described in step 5 increases Benefit process be：
    Terminate iterative cycles when meeting the condition of convergence represented by the 5th formula, according to the 7th formula, followed using each iteration The color gain that ring calculatesGenerate final color gain7th formula is：
    <mrow> <msubsup> <mi>G</mi> <mi>i</mi> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> </msubsup> <mo>=</mo> <munderover> <mo>&amp;Pi;</mo> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>T</mi> </munderover> <msubsup> <mi>g</mi> <mi>i</mi> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow>
    Wherein T represents whole iterationses.
  5. 5. color calibration method as claimed in claim 4, which is characterized in that described in step 6 using step 5 generate The process of final color gain correction all images is：According to the 8th formula correction all images, the 8th formula is：
    <mrow> <msubsup> <mover> <mi>I</mi> <mo>~</mo> </mover> <mi>i</mi> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> </msubsup> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <msubsup> <mi>I</mi> <mi>i</mi> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> </msubsup> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>&amp;times;</mo> <msubsup> <mi>G</mi> <mi>i</mi> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> </msubsup> </mrow>
    Wherein viWhole pixels of representative image i,Final color gain is represented,The color average of representative image i,Represent final color average.
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