Research On Spectrum Reconstruction Based On Multispectral Imaging System

Color is everywhere in daily life and is closely related to our lives.With the continuous development of science and technology,people's requirements for color reproduction of color images have also increased.Color reproduction technology has become a hotspot for color workers.At present,there are mainly two types of color reproduction technology,color reproduction of color appearance models and color reproduction of spectra.The former copied color has the same color visually,and the spectrum may not be consistent,which can not avoid the trouble caused by metamerism.Spectral color reproduction is the use of spectral reflectance as a medium for color information transmission and reproduction to ensure color consistency,and is now widely used.Traditional methods for obtaining spectral reflectance mainly include spectrophotometers and hyperspectral analyzers,but due to the limitations of the two,they often cause unnecessary trouble in practical applications.For this reason,color science and technology workers have proposed to use multispectral imaging technology to obtain spectral reflectance,and use the multi-channel image information output by the multispectral imaging system camera to estimate the spectral reflectance of the target sample.The process of estimation is called spectral reconstruction.Color digital cameras can take non-contact imaging of target samples under a variety of conditions,and digital cameras have the advantages of flexibility,convenience,and high cost performance.Therefore,this paper adopts a color digital camera equipped with a set of filters to form a multispectral imaging system.The signal reconstructs the surface reflectance of the object.At present,the commonly used methods for estimating the spectral reflectance of an object's surface by spectral reconstruction include pseudo-inverse method,principal component analysis method and BP neural network algorithm.Based on the shortcomings of the BP neural network reconstruction algorithm,a polynomial model and a Bayesian regularization correction term are introduced to improve the traditional BP neural network spectral reconstruction algorithm to optimize the algorithm and improve its accuracy.In order to verify the feasibility of the reconstruction algorithm,the training samples used the standard color card Digital Color Checker SG,and the test samples used the standard color card Color Checker Rendition Chart.The experimental results show that the spectral reflectance reconstructed by the algorithm proposed in this paper is superior to traditional neural network algorithms in terms of chrominance accuracy and spectral accuracy,and the accuracy is far greater than that of the pseudo-inverse method and principal component analysis method.The method has certain value for the true reproduction of the surface color of the object.Finally,the Bayesian regularized neural network spectral reconstruction algorithm proposed in this paper is used to obtain the reconstructed spectral reflection,which is combined with colorimetry and computer graphics imaging knowledge to reproduce the true reproduction of the color of the batik core.For the same color block in the picture core,under different angles of the light source,the color block after its reproduction is analyzed by HSI and L*a*b* color space.The experimental results show that the HSI color space has better stability,and can provide theoretical support for fitting any real-time image of the reference standard color block to any image work at any angle.