Single-sensor multispectral imaging technology has been widely used in computer vision, mechanical diagnosis, cultural history protection and other industries due to its convenience and low cost. Single-sensor multispectral imaging can only generate a single mosaic image, so an efficient method is needed to convert mosaic images into multispectral images. Based on the concept of pseudo-panchromatic image, a 9-band multispectral imaging system is designed in this paper. We directly estimate the pseudo-panchromatic image from the mosaic image and use the correlation between the pseudo panchromatic image and each channel to generate a multispectral image by guided filtering and residual interpolation. The experimental results show that the multispectral images obtained by our method are superior to the other two methods in objective and subjective evaluation.
A procedure for spectral recovery method based on color constancy is proposed from camera response values. The proposed method standardizes camera response values under different light sources by using different color constancy algorithms, and applied the pseudo inverse operation with the camera responses expansion to achieve the spectral reflectance recovery. To reduce the influence of external light source, the crossover combination of light sources and color constancy algorithms are calculated to determine the optimal selection of processing. The effectiveness of the color constancy algorithm was verified by using the spectral accuracy comparison and image similarity. The experimental results show that color constancy can significantly improve the spectral recovery accuracy, and the grey edge has the best performance.
The accuracy of spectral recovery is directly affected by the spectral filter. Clarifying which parameters of the color filter will affect the spectral reconstruction accuracy can help us select the appropriate color filter and improve the reconstruction accuracy. Gaussian curve is used to simulate the spectral sensitivity curve of camera CCD channel. Taking color difference, goodness of fit coefficient and root mean square error as the result evaluation parameters, three, five and seven spectral filters with bandwidth of 20-60nm are selected for experiments. The influence of the number and bandwidth of camera channels on the spectral recovery results is explored by calculating the spectral recovery accuracy of Munsell spectral data set. The experimental results show that the number and bandwidth of spectral filters will affect the spectral recovery results.
KEYWORDS: Reflectivity, Neurons, Principal component analysis, Convolutional neural networks, Seaborgium, Color difference, Printing, Data processing, Data modeling, Convolution
A procedure for spectral reflectance recovery from CIE tristimulus values is proposed using the convolutional neural network method. Unlike the common spectral recovery methods in a linear way, the nonlinear transformation from the CIE tristimulus values to spectral reflectance is to achieve in this paper. In consideration of the computation time and accuracy of spectral recovery, the internal parameters of convolutional neural network are adjusted by the number of neurons and the interval between neurons. The effectiveness of the proposed method and the previous methods are analyzed by calculating the spectral recovery accuracy under different spectral datasets and different error metrics. The results show that the proposed method is superior to traditional algorithms.
KEYWORDS: Associative arrays, Visualization, Spatial filters, Color reproduction, Color vision, Printing, Information visualization, Contrast sensitivity, Visual system, Data modeling
Due to the increasing globalization of printing industry, remoting proofing will become the inevitable development trend. Cross-media color reproduction will occur in different color gamuts using remote proofing technologies, which usually leads to the problem of incompatible color gamut. In this paper, to achieve equivalent color reproduction between a monitor and a printer, a frequency-based spatial gamut mapping algorithm is proposed for decreasing the loss of visual color information. The design of algorithm is based on the contrast sensitivity functions (CSF), which exploited CSF spatial filter to preserve luminance of the high spatial frequencies and chrominance of the low frequencies. First we show a general framework for how to apply CSF spatial filter in retention of relevant visual information. Then we compare the proposed framework with HPMINDE, CUSP, Bala’s algorithm. The psychophysical experimental results indicated the good performance of the proposed algorithm.
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