The acquisition of camera spectral sensitivity has become a hot issue in recent years because of its fundamental role in image processing and color reproduction. Although the estimation method based on camera response formation model has many advantages, the implementation procedures for spectral characterization are still tedious and time-consu…
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CameraResponsePrediction_training.m
DE2000BarPlot_SpecPlot_A_script.m
DE2000BarPlot_SpecPlot_script.m
LOOCV.m
LOOCV_A_result.mat
LOOCV_result.mat
README.md
SPD_Plot_script.m
TestingUsingMinorTrainingSamples_A_result.mat
TestingUsingMinorTrainingSamples_illuminantA_script.m
TestingUsingMinorTrainingSamples_result.mat
TestingUsingMinorTrainingSamples_script.m
TwoIlluminantCrossValidation_script.m

README.md

Impacting-factors-on-camera-calibration-for-spectral-sensitivity-estimation

The acquisition of camera spectral sensitivity has become a hot issue in recent years because of its fundamental role in image processing and color reproduction. Although the estimation method based on camera response formation model has many advantages, the implementation procedures for spectral characterization are still tedious and time-consuming. In order for the improvement of calibration efficiency, it was investigated in this study that how the changes of the training samples selection would influence the estimation results and how many color samples at least should be included to achieve a high-fidelity color reproduction.