Imaging pipeline for spectro-colorimeters

Abstract

A spectro-colorimeter system for imaging pipeline is provided, the system including a camera system; a spectrometer system; and a controller coupling the camera system and the spectrometer system. In some embodiments the camera system is configured to provide a color image with the first portion of the incident light. Also, in some embodiments the spectrometer system is configured to provide a tristimulus signal from the second portion of the incident light. Furthermore, in some embodiments the controller is configured to correct the color image from the camera system using the tristimulus signal from the spectrometer. An imaging pipeline method for using a system as above is also provided. Further, a method for color selection in an imaging pipeline calibration is provided.

What is claimed is: 
     
         1 . A spectro-colorimeter system for imaging pipeline comprising:
 a camera system including a separating component and a camera, the separating component directing a first portion of an incident light to the camera system;   a spectrometer system including an optical channel, a slit, and a spectroscopic resolving element, the separating component directing a second portion of the incident light to the spectrometer system through the optical channel; and   a controller coupling the camera system and the spectrometer system, wherein:
 the camera system is configured to provide a color image with the first portion of the incident light; 
 the spectrometer system is configured to provide a tristimulus signal from the second portion of the incident light; and 
 the controller is configured to correct the color image from the camera system using the tristimulus signal from the spectrometer. 
   
     
     
         2 . The spectro-colorimeter system of  claim 1 , wherein the spectrometer system comprises one from the group consisting of a Bayer-filter array a Foveon filter array, and a time-sequential configuration. 
     
     
         3 . The spectro-colorimeter system of  claim 1 , wherein the separating component includes one from the group consisting of a beam splitter and a mirror having an aperture. 
     
     
         4 . The spectro-colorimeter system of  claim 1 , wherein the optical channel comprises at least one from the group consisting of a transparent conduit, a lens, a mirror, and free space optics. 
     
     
         5 . The spectro-colorimeter system of  claim 1 , wherein the controller adjusts a camera system accuracy according to a spectrometer system accuracy. 
     
     
         6 . The spectro-colorimeter system of  claim 1 , wherein the resolving element is a diffraction grating or a prism. 
     
     
         7 . An imaging pipeline method comprising:
 providing a calibration target;   receiving Red, Blue, and Green (RGB) data from a camera system;   receiving tristimulus data (XYZ) from a spectrometer system;   providing a color correction matrix; and   providing an error correction to the camera system.   
     
     
         8 . The method of  claim 7 , wherein providing a calibration target comprising selecting a plurality of screen displays having standardized characteristics. 
     
     
         9 . The method of  claim 7 , wherein providing a color correction matrix includes finding a transformation between the RGB data and the XYZ data. 
     
     
         10 . The method of  claim 9 , wherein includes finding an order of a polynomial model relating the RGB data and the XYZ data. 
     
     
         11 . The method of  claim 10 , wherein finding an order of a polynomial model includes using a first order polynomial including a white color and a black color. 
     
     
         12 . The method of  claim 7 , wherein providing an error correction to the camera system includes using a least squares calculation between the XYZ data and the color corrected data. 
     
     
         13 . A method for color selection in an imaging pipeline calibration comprising:
 selecting a training sample;   including the training sample in a predictor set when the training sample is not already included;   obtaining a color correction matrix using the predictor set;   obtaining an error value using the color correction matrix and a plurality of test samples;   forming a set of predictor set error values from a plurality of predictor sets when no more training samples are selected;   selecting a training sample and a new predictor set from the set of predictor set error values; and   providing the color correction matrix and the new predictor set when an error value associated to the new predictor set is less than a tolerance.   
     
     
         14 . The method of  claim 13  wherein selecting a training sample comprises selecting a training sample set from a standardized set. 
     
     
         15 . The method of  claim 13 , wherein obtaining a color correction matrix using the predictor set comprises finding a transformation between a Red, Green, Blue (RGB) data provided by a camera system and a tristimulus (XYZ) data provided by a spectrometer system. 
     
     
         16 . The method of  claim 15 , wherein finding a transformation between the RGB data and the XYZ data comprises finding an order of a polynomial model relating the RGB data and the XYZ data. 
     
     
         17 . The method of  claim 16 , wherein finding an order of a polynomial model includes using a first order polynomial including a white color and a black color. 
     
     
         18 . The method of  claim 15 , wherein obtaining an error value comprises using a least squares calculation between the XYZ data and the color corrected data to find an error value for each of the training samples in the predictor set. 
     
     
         19 . The method of  claim 18 , wherein obtaining an error value comprises selecting the smallest of the error values for the training samples in the predictor set. 
     
     
         20 . The method of  claim 13 , further comprising selecting a new training sample and increasing a dimension of the predictor set when the error value associated with the new predictor set is larger than the tolerance.