CN109272463A - A kind of mural painting color recovery method - Google Patents
A kind of mural painting color recovery method Download PDFInfo
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- CN109272463A CN109272463A CN201811034936.0A CN201811034936A CN109272463A CN 109272463 A CN109272463 A CN 109272463A CN 201811034936 A CN201811034936 A CN 201811034936A CN 109272463 A CN109272463 A CN 109272463A
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- 238000010422 painting Methods 0.000 title claims abstract description 44
- 238000011084 recovery Methods 0.000 title claims abstract description 35
- 238000000034 method Methods 0.000 title claims abstract description 28
- 230000003595 spectral effect Effects 0.000 claims abstract description 56
- 238000002310 reflectometry Methods 0.000 claims abstract description 50
- 238000004611 spectroscopical analysis Methods 0.000 claims abstract description 31
- 238000001228 spectrum Methods 0.000 claims abstract description 28
- 230000009467 reduction Effects 0.000 claims abstract description 20
- 238000003384 imaging method Methods 0.000 claims abstract description 8
- 239000011159 matrix material Substances 0.000 claims description 28
- 230000006978 adaptation Effects 0.000 claims description 9
- 238000006243 chemical reaction Methods 0.000 claims description 4
- 230000009466 transformation Effects 0.000 claims description 4
- 238000010606 normalization Methods 0.000 claims description 3
- 241000208340 Araliaceae Species 0.000 claims 1
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 claims 1
- 235000003140 Panax quinquefolius Nutrition 0.000 claims 1
- 235000008434 ginseng Nutrition 0.000 claims 1
- 239000003086 colorant Substances 0.000 abstract description 6
- 230000004044 response Effects 0.000 abstract description 4
- 241000532370 Atla Species 0.000 abstract description 3
- 238000005516 engineering process Methods 0.000 abstract description 3
- 238000003672 processing method Methods 0.000 abstract description 2
- 238000000985 reflectance spectrum Methods 0.000 abstract description 2
- 238000009877 rendering Methods 0.000 description 8
- 230000000694 effects Effects 0.000 description 5
- 230000008901 benefit Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000004301 light adaptation Effects 0.000 description 2
- 239000013598 vector Substances 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000000701 chemical imaging Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 230000002969 morbid Effects 0.000 description 1
- 238000004806 packaging method and process Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000011946 reduction process Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 238000000844 transformation Methods 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/90—Determination of colour characteristics
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
- G06T2207/10036—Multispectral image; Hyperspectral image
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Abstract
The present invention provides a kind of mural painting color recovery methods, comprising the following steps: step 1, using the spectroscopic data of multi-optical spectrum imaging system acquisition parked mural painting, obtains the spectroscopic data of parked；Step 2, dimension-reduction treatment is carried out to the spectroscopic data of parked, the spectroscopic data after obtaining dimensionality reduction；Step 3, the spectral reflectivity of the spectroscopic data after dimensionality reduction is rebuild, the spectral reflectivity rebuild, color recovery is carried out to the spectroscopic data after dimensionality reduction using the spectral reflectivity of reconstruction, the color data after being restored.The present invention acquires the multichannel response data of colour atla and mural painting using multi-optical spectrum imaging system, the spectral reflectivity of wall painting surface is calculated using multiple spectrum algorithm for reconstructing, the color recovery of mural painting is realized in conjunction with relevant colors theory and spectrum picture processing method, the reflectance spectrum of object is rebuild to realize the recovery of object true colors using multi-optical spectrum imaging technology, can satisfy the high-precision needs of historical relic arts reproduction.
Description
Technical field
The invention belongs to digital image processing fields, and in particular to a kind of mural painting color recovery method.
Background technique
At this stage, the color recovery of multispectral technology is applied to the color recovery in the fields such as poster, packaging mostly, for simultaneously
It is not directed to mural painting color recovery field；Traditional color recovery mode has color recovery based on coloration, correct color simultaneously
It restores, color recovery and the color recovery of corresponding color etc. of equal value, because of the presence of metamerism phenomenon, although these modes
It can successfully realize the recovery of color, but be difficult to control because the factor for influencing restoration result accuracy morely, cause it to environment
Equal factors dictates are excessively harsh, cannot achieve the unconditional recovery of color.
Summary of the invention
For the deficiencies in the prior art, the object of the present invention is to provide a kind of mural painting color recovery method, solutions
Certainly the prior art cannot achieve the technical issues of mural painting color is unconditionally restored.
In order to solve the above-mentioned technical problem, the application, which adopts the following technical scheme that, is achieved:
1, a kind of mural painting color recovery method, comprising the following steps:
Step 1, using the spectroscopic data of multi-optical spectrum imaging system acquisition parked mural painting, the spectrum number of parked is obtained
According to；
Step 2, dimension-reduction treatment is carried out to the spectroscopic data of parked, the spectroscopic data after obtaining dimensionality reduction；
Step 3, the spectral reflectivity of the spectroscopic data after dimensionality reduction is rebuild, the spectral reflectivity rebuild, benefit
Color recovery is carried out to the spectroscopic data after dimensionality reduction with the spectral reflectivity of reconstruction, the color data after being restored.
Further, the spectral reflectivity of the spectroscopic data after dimensionality reduction is rebuild in the step 3, is rebuild
Spectral reflectivity carries out color recovery to the spectroscopic data after dimensionality reduction using the spectral reflectivity of reconstruction, the face after being restored
Chromatic number evidence, comprising:
Step 3.1, the spectral reflectivity rebuild by formula (4)
In formula (1), A is the color matching functions matrix in CIE standard, and A=hsv, h are the tristimulus values in CIE standard
Parameter, s are the power distribution of the lighting source in CIE standard, and v is observer's adaptation function in CIE standard；For any drop
Spectroscopic data g after dimension1Corresponding spectral reflectivity；I is unit matrix；T is the tristimulus values in CIE standard；
Step 3.2, the RBG data by formula (6) according to the spectral reflectivity after reconstruction, after being restored；
In formula (5), the spectral reflectivity that r (λ), which is wavelength, to be rebuild when being λ,It is 380nm~780nm model for wavelength
Spectral reflectivity of the visible light after 20nm is sampled in enclosing；L (λ) is the irradiation of specific light source specified in CIE standard
Under function of spectral power distribution；For the XYZ observation under the irradiation of specific light source specified in CIE standard
Person's light adaptation function；ρ is the normalization coefficient under the irradiation of specific light source specified in CIE standard；
Step 3.3, the RBG data conversion after recovery is divided at r, g, b of graphic color range [0,255] by formula (7)
It measures to get the color data to after restoring；
In formula (7), γ+(R)、γ+(G) and γ+(B) be respectively R, G, B γ inverse transformation.
Further, the step 3.3 can be with are as follows:
The spectral reflectivity rebuild by formula (8)
In formula (8), A is the color matching functions matrix in CIE standard, and A=hsv, h are the tristimulus values in CIE standard
Parameter, s are the power distribution of the lighting source in CIE standard, and v is observer's adaptation function in CIE standard；For any drop
Spectroscopic data g after dimension1Corresponding spectral reflectivity；I is unit matrix；T is the tristimulus values in CIE standard；W is canonical
Change parameter.
Compared with prior art, the present invention beneficial has the technical effect that
The present invention uses multiple spectrum weight using the multichannel response data of multi-optical spectrum imaging system acquisition colour atla and mural painting
The spectral reflectivity that algorithm calculates wall painting surface is built, the face of mural painting is realized in conjunction with relevant colors theory and spectrum picture processing method
Color restores, and rebuilds the reflectance spectrum of object using multi-optical spectrum imaging technology to realize the recovery of object true colors, can satisfy
The high-precision of historical relic arts reproduction needs.
Detailed description of the invention
Fig. 1 (a) is that mural painting refers to color lump schematic diagram；It (b) is multispectral imaging image；
Fig. 2 is the spectral reflectivity that color color lump is rebuild；
Fig. 3 is the comparison diagram of color color lump CIE LAB chrominance space distribution；
Fig. 4 (a) is the original mural painting of D65 light source rendering；Fig. 4 (b) is the original mural painting of A light source rendering；Fig. 4 (c) is D65
The recovery mural painting of light source rendering；The recovery mural painting of Fig. 4 (d) A light source rendering.
Explanation is further explained in detail to particular content of the invention below in conjunction with drawings and examples.
Specific embodiment
Specific embodiments of the present invention are given below, it should be noted that the invention is not limited to implement in detail below
Example, all equivalent transformations made on the basis of the technical solutions of the present application each fall within protection scope of the present invention.
Embodiment 1:
The present embodiment provides a kind of mural painting color recovery method, comprising the following steps:
Step 1, using the spectroscopic data of multi-optical spectrum imaging system acquisition parked mural painting, the spectrum number of parked is obtained
According to；
Step 2, dimension-reduction treatment is carried out to the spectroscopic data of parked, the spectroscopic data after obtaining dimensionality reduction；
The spectral reflectance data obtained after dimensionality reduction in the present embodiment can be expressed as M unequal one-dimensional spectral reflectances
Rate forms matrix, that is, r=[r1r2…rM]T
Its reduction process are as follows:
Obtain k orthogonal basis vectors BiLinear combination, it may be assumed that
In formula, B=[B1B2...Bk] it is characterized vector matrix, a=[a1a2…ak]TFor transition matrix.Set M not phases
Deng one-dimensional spectral reflectivity form matrix r=[r1r2…rM]T；
Step 3, the spectral reflectivity of the spectroscopic data after dimensionality reduction is rebuild, the spectral reflectivity rebuild, benefit
Color recovery is carried out to the spectroscopic data after dimensionality reduction with the spectral reflectivity of reconstruction, the color data after being restored.
The present invention is rebuild using spectral reflectivity of the R matrix method to the spectroscopic data after dimensionality reduction, comprising:
Step 3.1, according to the theory of R matrix, matrix R can be regarded as an orthogonal project operator, by observer and Te
Determine the expression of lighting condition collective effect matrix A, i.e. A=hsv, h are tristimulus values parameter, and s is that the power of lighting source is distributed, v
For observer's adaptation function, therefore R=A (ATA)-1AT。
Therefore spectral reflectivity is rebuild according to R matrix methodAre as follows:
Wherein, r ' is basic stimulus spectrum, b1It is homochromy different general black；
The power of tristimulus values parameter h in the present embodiment, lighting source are distributed s, observer adaptation function v, tristimulus
Value t is both from CIE standard.
Basic stimulus spectrum r ' is obtained by formula (1):
R '=Rr (1)
In formula (1), R=A (ATA)-1AT, r is the spectral reflectivity of parked mural painting；
Homochromy different general black b is obtained by formula (3)1:
In formula (3),Q is transformed matrix, g1To appoint
Sample to be tested collection (the g of meaning0,r0) channel response value,For g0Inverse conversion matrix, r0For the spectral reflectivity of different wave length
The matrix of composition.
In formula (3),Q is transformed matrix, g1It is arbitrary
The channel response value of parked mural painting collection.
Step 3.2, the RBG data by formula (5), formula (6) according to the spectral reflectivity after reconstruction, after being restored；
In formula (5), the spectral reflectivity that r (λ), which is wavelength, to be rebuild when being λ,It is 380nm~780nm model for wavelength
Spectral reflectivity of the visible light after 20nm is sampled in enclosing；L (λ) is the irradiation of specific light source specified in CIE standard
Under function of spectral power distribution；For the XYZ observation under the irradiation of specific light source specified in CIE standard
Person's light adaptation function；ρ is the normalization coefficient under the irradiation of specific light source specified in CIE standard；
Wherein, CIE standard is the standard colorimetric system of International Commission on Illumination's defined, and specific light source is D65 standard
Light source；
Step 3.3, the RBG data conversion after recovery is divided at r, g, b of graphic color range [0,255] by formula (7)
Amount；
In formula (7), γ+(R)、γ+(G) and γ+(B) be respectively R, G, B γ inverse transformation.
Embodiment 2:
The present embodiment the difference from embodiment 1 is that, the spectral reflectivity of reconstructionIt can be with are as follows:
In formula (8), A is color matching functions matrix, and A=hsv, h are tristimulus values parameter, and s is the power of lighting source
Distribution, v are observer's adaptation function；For arbitrary sample to be tested signal value g1, calculate corresponding spectral reflectivity；I is unit
Matrix；T is tristimulus values；W is regularization parameter.
It is available from formula (4), existing R matrix method when rebuilding spectral reflectivity, need to obtain homochromy different general black b and
Basic stimulus spectrum r ', but basic stimulus spectrum r ' needs to solve ATPseudoinverse, influenced, solved pseudo- by particular light condition
The process of inverse operation is unstable, it will obtains the solution of equation of morbid state, can impact to the spectral accuracy of reconstruction.
In order to solve this problem the application, needs R matrix method to be added regularization limitation, to avoid ill-conditioning problem.
After Tikhonov limitation is added, the spectral reflectivity of the R matrix method reconstruction based on Tikhonov regularization are as follows:
In formula (8), A is the color matching functions matrix in CIE standard, and A=hsv, h are the tristimulus values in CIE standard
Parameter, s are the power distribution of the lighting source in CIE standard, and v is observer's adaptation function in CIE standard；For any drop
Spectroscopic data g after dimension1Corresponding spectral reflectivity；I is unit matrix；T is the tristimulus values in CIE standard；W is canonical
Change parameter.
Experimental verification:
The present embodiment using the sample in Raul's colour atla as training sample, optional width mural painting as parked mural painting,
Such as Fig. 1, carry out rebuilding spectrums using tri- kinds of rebuilding spectrum algorithms of PCA, MR and RMR respectively, the ratio that gets colors in mural painting compared with
Six big regions carry out experimental study, by being obtained under the irradiation of international standard alpine light D65 light source using multispectral camera
Multichannel image signal data of the mural painting in the case where being a spike interference filter is taken, is test with 6 reference color lumps in mural painting
Sample evaluates the reconstruction precision of three kinds of rebuilding spectrum algorithms, and Appreciation gist is respectively mural painting with reference to color lump reconstruction spectrum
Color difference Δ E, root-mean-square error (RMSE), fitness coefficient (GFC) and the inclined index of Spectral matching (ISSD).Utilize RMR method
To the spectral reflectivity curve after the color reconstruction of 6 color lumps in mural painting as shown in Fig. 2, the number designation of color lump is right respectively in figure
What is answered is the digital color region of mural painting in Fig. 1 (a).
The experimental results showed that being above MR and PCA method using the rebuilding spectrum precision of six color lumps of RMR method.It says
It is bright to obtain higher spectrum and coloration precision using RMR method progress rebuilding spectrum.
In order to intuitively reflect the rebuilding spectrum coloration precision of RMR, MR and PCA method, 6 colors in mural painting are calculated
The L of block color*a*b*Chromatic value (L*Indicate brightness, a*Indicate the range from red to green, b*Indicate the model from yellow to blue
Enclose), with L*a*b*In chromatic component a*, b*Respectively reference axis, as a result as shown in figure 3, from chromatic value spatial distribution it is found that
The L of RMR method rebuilding spectrum*a*b*The L of chromatic value and original spectrum*a*b*Color measurements are more close, and color lump all has in figure
Effect is rebuild well, illustrates that RMR method ratio MR and PCA method have better stability.Mural painting light is rebuild using RMR method
Compose reflectivity after respectively international standard alpine light D65 light source and tengsten lamp A light source rendering under original image and restored map
As shown in Figure 4, it can be seen that the recovery under D65 light source and A light source scene has reached preferable effect.In conjunction with Fig. 4 (a)
It is compared from (c), (b) and (d) it can also be seen that the mural painting of the lower recovery of different light sources rendering is more apparent than the texture of original mural painting,
Color is more bright-coloured, the blue presented such as color lump 2.And the mural painting of D65 light source rendering is closer to original mural painting effect, A light source
A bit, this puts the color temperature characteristic for also complying with D65 and A light source to the whole warm colour partially of the mural painting of rendering.Further explanation illustrates RMR method
There is better stability than MR and PCA method.
Claims (3)
1. a kind of mural painting color recovery method, which comprises the following steps:
Step 1, using the spectroscopic data of multi-optical spectrum imaging system acquisition parked mural painting, the spectroscopic data of parked is obtained；
Step 2, dimension-reduction treatment is carried out to the spectroscopic data of parked, the spectroscopic data after obtaining dimensionality reduction；
Step 3, the spectral reflectivity of the spectroscopic data after dimensionality reduction is rebuild, the spectral reflectivity rebuild, utilizes weight
The spectral reflectivity built carries out color recovery to the spectroscopic data after dimensionality reduction, the color data after being restored.
2. mural painting color recovery method according to claim 1, which is characterized in that the light after dimensionality reduction in the step 3
The spectral reflectivity of modal data is rebuild, the spectral reflectivity rebuild, using the spectral reflectivity of reconstruction to dimensionality reduction after
Spectroscopic data carry out color recovery, the color data after being restored, comprising:
Step 3.1, the spectral reflectivity rebuild by formula (4)
In formula (1), A is the color matching functions matrix in CIE standard, and A=hsv, h are the tristimulus values parameter in CIE standard,
S is the power distribution of the lighting source in CIE standard, and v is observer's adaptation function in CIE standard；After any dimensionality reduction
Spectroscopic data g1Corresponding spectral reflectivity；I is unit matrix；T is the tristimulus values in CIE standard；
Step 3.2, the RBG data by formula (6) according to the spectral reflectivity after reconstruction, after being restored；
In formula (5), the spectral reflectivity that r (λ), which is wavelength, to be rebuild when being λ,It is within the scope of 380nm~780nm for wavelength
Spectral reflectivity of the visible light after 20nm is sampled；L (λ) is under the irradiation of specific light source specified in CIE standard
Function of spectral power distribution；For XYZ observer's light under the irradiation of specific light source specified in CIE standard
Adaptation function；ρ is the normalization coefficient under the irradiation of specific light source specified in CIE standard；
Step 3.3, by formula (7) by the RBG data conversion after recovery at r, g, b component of graphic color range [0,255], i.e.,
Color data after being restored；
In formula (7), γ+(R)、γ+(G) and γ+(B) be respectively R, G, B γ inverse transformation.
3. mural painting color recovery method according to claim 2, which is characterized in that the step 3.3 can be with are as follows:
The spectral reflectivity rebuild by formula (8)
In formula (8), A is the color matching functions matrix in CIE standard, and A=hsv, h are the tristimulus values parameter in CIE standard,
S is the power distribution of the lighting source in CIE standard, and v is observer's adaptation function in CIE standard；After any dimensionality reduction
Spectral reflectivity corresponding to spectroscopic data g1；I is unit matrix；T is the tristimulus values in CIE standard；W is regularization ginseng
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| CN110020998A (en) * | 2019-04-12 | 2019-07-16 | 曲阜师范大学 | Faded color Chinese Painting and Calligraphy color recovery method based on spectrum |
| CN110254116A (en) * | 2019-06-26 | 2019-09-20 | 季云博 | The reparation of the Longmen Grottoes colour mural painting, the non-something lost technique for drawing color |
| CN112747903A (en) * | 2020-12-28 | 2021-05-04 | 南京林业大学 | Optimal light source spectral power determination method based on colorimetry color replication |
| CN113160077A (en) * | 2021-04-08 | 2021-07-23 | 武汉纺织大学 | High-fidelity digital restoration method for color of faded colored mural |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110020998A (en) * | 2019-04-12 | 2019-07-16 | 曲阜师范大学 | Faded color Chinese Painting and Calligraphy color recovery method based on spectrum |
| CN110254116A (en) * | 2019-06-26 | 2019-09-20 | 季云博 | The reparation of the Longmen Grottoes colour mural painting, the non-something lost technique for drawing color |
| CN112747903A (en) * | 2020-12-28 | 2021-05-04 | 南京林业大学 | Optimal light source spectral power determination method based on colorimetry color replication |
| CN112747903B (en) * | 2020-12-28 | 2022-07-26 | 南京林业大学 | Optimal light source spectral power determination method based on colorimetry color replication |
| CN113160077A (en) * | 2021-04-08 | 2021-07-23 | 武汉纺织大学 | High-fidelity digital restoration method for color of faded colored mural |
| CN113160077B (en) * | 2021-04-08 | 2022-05-13 | 武汉纺织大学 | High-fidelity digital restoration method for color of faded colored mural |
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