Spectral image processing method

Abstract

The invention relates to a spectral image processing method. The method includes: constructing a weighting matrix through three kinds of vision cone spectral responses of human eyes, multiplying an original spectral image by the weighting matrix prior to principal component analysis, taking first three feature vectors as new first three vectors of a six-dimensional linear model, then performing principal component analysis by residual spectra, taking first three feature vectors as new last three vectors of the six-dimensional linear model, using the six base vectors for performing dimensionality reduction on the original high-dimensional spectral image to obtain a six-dimensional multi-channel image, and then replacing the original high-dimensional spectral image with the six-dimensional multi-channel image to be used for the process of color reproduction. When the spectral image is processed, human vision features and a spectral linear dimension reduction model are combined skillfully, and matching precision in two aspects of spectra and chromaticity is taken into consideration, so that the spectral image with high precision, high imitation and optimum color matching and reduction effect can be restored and reproduced.

Description

[0001] 【Technical field】 [0002] The invention relates to the field of spectrum image compression and reproduction, in particular to a method for completing reproduction by combining the human eye cone response with spectral image compression. [0003] 【Background technique】 [0004] The processing methods of using spectral images for color reproduction are mainly divided into two categories. One is to directly use high-dimensional original images for color reproduction. In this reproduction process, complex calculations are required to use all spectral information for color Copy, due to the high computational complexity, will need to occupy system memory for calculation, and the entire calculation will take a long time. More importantly, a large part of spectral information in high-dimensional spectral images is tedious and redundant information for color reproduction. When the high-dimensional spectral image is directly used for color reproduction, a large amount of calcula...

AI Technical Summary

Problems solved by technology

However, the defect of this type of method is that the spectral image is dimensionally reduced and compressed by mathematical statistics. The reconstructed spectral image is a mathematical approximation of the original spectral image, rather than a complete and accurate copy. There is visual distortion, not color Best Match and Restore on

When performing dimensionality reduction and compression processing on the original spectral image, methods such as principal component analysis and independent component analysis are generally used. The original information data is replaced by statistical results. When restoring, the original information cannot be completely reconstructed, resulting in only reconstruction Mathematical approximation results for spectral images

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