This repository contains the evaluation code for the paper "Color Correction using Splines" presented at Electronic Imaging 2024.
spline_tuning.ipynb contains the Jupyter Notebook for finding appropriate smoothing parameters for the spline models.
model_evaluation.ipynb contains the actual evaluation script for linear, root-polynomial, polynomial and spline models.
Data is available here: https://drive.google.com/drive/folders/1bgSEbViS9_vTcakd6Ubv9H90GpXxBUfR?usp=drive_link
All credits due to The Columbia Imaging and Vision Laboratory, Simon Frasier University Computer Vision Lab and David Foster for their datasets, which I composed together:
https://www.cs.columbia.edu/CAVE/databases/multispectral/ https://www2.cs.sfu.ca/~colour/data/colour_constancy_synthetic_test_data/index.html#DESCRIPTION https://personalpages.manchester.ac.uk/staff/d.h.foster/Hyperspectral_images_of_natural_scenes_04.html https://personalpages.manchester.ac.uk/staff/d.h.foster/50_Reduced_Hyperspectral_Reflectance_Images/50_Reduced_Hyperspectral_Reflectance_Images.html