
- Photonics Research
- Vol. 4, Issue 3, 0115 (2016)
Abstract
1. INTRODUCTION
A conventional RGB camera can only obtain chrominance information under specific conditions. The color data are affected by the characteristics of the device and the illumination environment. It cannot reveal the objective color information. RGB data change when the illuminating environment changes. Spectral reflectance data can objectively characterize the color data, as much as possible to avoid the inauthenticity of color replication caused by metamerism. Therefore, the study of spectral reflectance data acquisition becomes the focus of current research. The spectral reflectance reconstruction method that is used to obtain multispectral images uses the pseudo-inverse reconstruction [
The problem above can be solved by compressing the totality of spectral channels into a smaller set that can be more easily managed. This set is made by taking a few linear combinations of the integrand products of the illuminant spectrum, narrow-band filter shapes, and the spectral sensitivity of the camera so as to approximate the first few principal components of a particular basic training sample spectral reflectance database. Multiplying these principal components by the testing spectral reflectance can be well fitted by a linear combination of the same principal components; the principal component vectors can be eliminated because of the orthogonality of the vectors, and then the fitted coefficients of the testing spectral reflectance based on the principal component vectors can be obtained and the testing spectral reflectance can be reconstructed.
This paper uses an illuminating source with a specific relative spectral power distribution to illuminate the objects and to modulate the spectral reflectance information of the object. The spectral energy is collected by a single-pixel detector, and high-resolution spectral reflectance information is reconstructed by using the method of sparse decomposition based on the principal component orthogonal base. In this paper, the research can make full use of the sparse prior knowledge (which means that the spectral reflectance are mostly zeros, with only occasionally nonzero values) of spectral reflectance and the relative spectral power distribution of the illuminating source based on the principal component orthogonal basis, which reduces the optical complexity of the multispectral data acquisition system and reduces the sampling number; the efficiency of spectral reflectance reconstruction is improved, and the reconstruction accuracy is improved. It has a certain reference significance for improving the multispectral image acquisition technology.
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2. PRESENT SPECTRAL REFLECTANCE RECONSTRUCTION ALGORITHM
After the multichannel spectral imaging system shoots the scene, it outputs a multichannel image that is related to the color space of the camera. The image is affected by the illumination and spectral reflectance of the scene and the spectral characteristics of the camera: where
This is shown as the discrete matrix
3. METHOD OF SPECTRAL REFLECTANCE RECONSTRUCTION BASED ON SPARSE PRIOR KNOWLEDGE BY PRINCIPAL COMPONENT ORTHOGONAL BASIS
According to the smooth characteristic of the spectral reflectance,
The particular measurement matrix
Then
The
In Eq. (
The method principle of spectral reflectance reconstruction based on sparse prior knowledge by a single-pixel detector of the principal component orthogonal basis is shown in Fig.
Figure 1.Spectral reflectance reconstruction principle based on the sparse prior by a single-pixel detector.
For the acquisition of spectral reflectance of the large-sized object, the spectral reflectance energy value of modulated objects is collected by using a CCD. (Each pixel of the CCD is made as a single-pixel detector to measure the reflecting light energy that is reflected from the corresponding position of the object.) Then the spectral reflectance of the object is obtained.
4. RESEARCH ON SIMULATION
The simulation of the spectral reflectance reconstruction based on sparse prior knowledge by a single-pixel detector of the principal component orthogonal basis is carried out in the MATLAB simulation platform. The experiment uses a piece of the training sample set of 1296 Munsell color as multispectral reflectance data of the testing sample; the detecting range of reflectance is from 380 to 780 nm, and the sampling interval is 5 nm. The relative spectral power distribution of the modulation illuminating source illuminates the color lump. A single-pixel detector is used to collect the energy value when each modulated illuminating source illuminates the multispectral color lump.
The process of the spectral reflectance reconstruction based on sparse prior knowledge by a single-pixel detector of the principal component orthogonal basis is as follows: (1) the 1296 Munsell color card is selected as the basic training sample, and the first three principal components of spectral reflectance data are obtained by the method of principal component analysis, as shown in Fig.
Figure 2.Principal component of training sample set of 1296 Munsell colors.
Figure 3.Relative spectral power distribution of modulated illuminating light based on the principal component of training sample set of 1296 Munsell color. (a) Relative spectral power distribution of modulating light based on the first principal component. (b) Relative spectral power distribution of modulating light based on the second principal component. (c) Relative spectral power distribution of modulating light based on the third principal component.
Figure 4.Spectral reflectance of a piece of training sample set of 1296 Munsell color.
Figure 5.Results of spectral reflectance reconstruction based on the sparse prior by a single-pixel detector.
Method | Min | Max | Mean |
Pinv | 0.010077 | 0.02516 | 0.0129 |
A single detector | 0.002325 | 0.00405 | 0.00257 |
Table 1. Reconstruction Error of Spectral Reflectance
The multispectral reflectance and the relative spectral power distribution of the illuminating source can be sparse decomposition based on sparse prior knowledge of the principal component orthogonal basis, and the orthogonal property of the principal component orthogonal basis is used to eliminate basis. Then the sparse coefficient is obtained by calculation and the spectral reflectance data are obtained. The high accuracy spectral reflectance data are obtained with only a small amount of sampling. The detector is a single-pixel detector without spatial resolution ability, which reduces the cost of the multispectral image.
A. Effect of the Number of Base Vectors of Principal Component Orthogonal Basis
The training sample set of 1296 Munsell color is selected as the basic training sample, and the first four principal components of spectral reflectance data are obtained by principal component analysis. The relative spectral power distribution of illuminating light is modulated and constructed by using one, two, three, and four principal components, respectively, and a piece of the training sample of 1296 Munsell color is illuminated and selected as the testing sample. The spectral energy value obtained by a single-pixel detector is made as
Figure 6.Effect of the number of base vectors of principal component orthogonal basis.
From Fig.
B. Effect of Sample Selection
The color card of X-Rite 24,140, the Pantone cotton color card, and the 1296 Munsell color card are used to be the training sample set, and the chromaticity spatial distribution maps of the selected training sample set are drawn up, and shown in Figs.
Figure 7.Chromaticity spatial distribution of training sample sets (a) X-Rite 24, (b) X-Rite 140, (c) Pantone, and (d) Munsell.
It can be seen from Figs.
Figure 8.Reconstruction error of selecting different training samples.
5. EXPERIMENT
A piece of a training sample set of 1296 Munsell color is made as multispectral reflectance data. The acquisition range is from 610 to 670 nm, the sampling interval is 10 nm, and the relative spectral power distribution of illumination light is modulated; the relative spectral power distribution of modulation illuminating light is structured by selecting the principal component of the training sample set of the 1296 Munsell color card. A CH253 voltage type photon counter whose spectrum sensitivity changes gently from 610 to 670 nm (the influence of detection spectrum sensitivity is eliminated) is selected to collect the energy value by each modulation illuminating source illuminating the multispectral color card.
An illuminating source for which the center wavelength of the LED is 610, 620, 630, 640, 650, 660, and 670 nm is selected to compose the illuminating source (because the LED has a certain bandwidth, the sampling interval is 10 nm), and the relative spectral power distribution of the illuminating source is modulated by controlling the control current and add attenuator before the single LED, respectively. It is shown in Fig.
Figure 9.Relative spectral power distribution of the LED.
Figure 10.Experimental results of spectral reflectance reconstruction based on sparse prior by a single detector.
A. Effect of the Number of Principal Components Base Vector
The 1296 Munsell color is selected as the basic training sample, and the first four principal components of multispectral reflectance data are obtained by the principal component analysis method. The relative spectral power distribution of the illuminating source is modulated and structured by using one, two, three, and four principal components. A piece of the training sample set of 1296 Munsell color is illuminated, and the spectral energy value collected by a single-pixel detector is made as the
Figure 11.Effect of the number of the principal component base vector.
It can be seen from Fig.
B. Effect of Sample Selection
The relative spectral power distribution of the illuminating source is structured by selecting the first three principal components of the training sample set of X-Rite 24,140, the Pantone cotton color card, and 1296 Munsell color, and a piece of the training sample of 1296 Munsell color is used to be the testing sample. The spectral energy value detected by a single-pixel detector is used to reconstruct the spectral reflectance by Eq. (
Figure 12.Effect of training sampling selection.
It can be seen from Fig.
6. CONCLUSION
At present, multispectral images generally use a grating spectrometer, a mechanical rotating filter, a liquid crystal tunable optical filter, and snapshot multispectral imaging to split, and the optical system complexity is higher, the price is expensive, and the craftwork is complex. This paper makes full use of the sparse features of spectral reflectance and the relative spectral power distribution of the illuminating source based on sparse prior knowledge by the principal component orthogonal basis, and the object is illuminated by using a lighting source with a specific relative spectral power distribution. The spectral reflectance information of the object is modulated, and high-resolution spectral reflectance information is reconstructed by using a single-pixel detector to collect the spectral energy. The method can reduce the optical complexity of the multispectral data acquisition system and reduce the sampling number, and the efficiency of spectral reflectance reconstruction is improved.
By studying the traditional spectral reflectance reconstruction method, spectral reflectance and the relative spectral power distribution of the lighting source based on
The spectral reflectance reconstruction method based on sparse prior knowledge by a single-pixel detector can use a smaller sampling amount to obtain high precision spectral reflectance data. The detector is a single-pixel detector without spatial resolution ability, which reduces the complexity and cost of the system, and it has certain reference significance for the improvement of multispectral image acquisition technology.
ACKNOWLEDGMENT
Acknowledgment. This study is supported by the National Natural Science Foundation of China (Grant No. 61405115), the Natural Science Foundation of Shanghai (Grant No. 14ZR1428400), and the Innovation Project of Shanghai Municipal Education Commission (Grant No. 14YZ099), National Basic Research Program of China (973 Program) (Grant No. 2015CB352004).
References
[5] E. A. Day, R. S. Berns, L. A. Taplin. A psychophysical experiment evaluating the color accuracy of several multispectral image capture techniques. J. Imaging Sci. Technol., 48, 93-104(2004).

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