Data from: Systematic Color Correction Pipeline for Controlled-Environment Imaging

Raw data set to evaluate a stepwise color correction (CC) pipeline for controlled imaging environments. The workflow integrates flat-field correction (FFC), gamma correction (GC), and white-balance correction (WB), followed by a color-mapping (CM) stage using machine-learning regression—linear, partial least squares (PLS), and neural networks (NN)—to deliver reliable CC in digital images. The pipeline reduces perceptual color differences in the corrected images. An NN with a second-degree polynomial expansion consistently outperformed other CM methods, yielding the lowest color errors and robust performance across varying imaging conditions.Tests showed that illumination quality and placement are critical: although the common 45° geometry produces favorable uncorrected images, top-mounted area lighting combined with FFC yielded the best corrected color. Imaging-environment materials also mattered; object background color and sidewalls affected fidelity, with diffusely reflective white performing best. Applied to various colored fruit samples, the proposed pipeline produced more consistent fruit colors across illuminants. An open-source Python package (https://github.com/collinswakholi/ColorCorrectionPackage) and an interactive user interface (https://github.com/collinswakholi/ColorCorrectionPackage_UI) implementing this pipeline is available, enabling reproducible analyses and straightforward adaptation to other controlled imaging tasks. Overall, the pipeline improved color reproduction and measurement in digital images and helped bridge the gap between sophisticated CC methods and practical, routine applications.本数据集用于评估适配受控成像环境的逐步骤色彩校正（Color Correction, CC）流程。该流程整合了平场校正（Flat-Field Correction, FFC）、伽马校正（Gamma Correction, GC）与白平衡校正（White-Balance Correction, WB），随后进入采用机器学习回归方法的色彩映射（Color-Mapping, CM）阶段——涵盖线性回归、偏最小二乘（Partial Least Squares, PLS）与神经网络（Neural Networks, NN）——以实现数字图像中的可靠色彩校正。该流程可降低校正后图像中的感知色差。采用二阶多项式展开的神经网络始终优于其他色彩映射方法，可实现最低的色彩误差，并在不同成像条件下保持稳定的性能表现。实验结果表明，光照质量与布局至关重要：尽管常见的45°几何布局可生成效果较佳的未校正图像，但顶面区域光源配合平场校正可得到最优的色彩校正效果。成像环境的材质同样会产生影响：物体背景色与侧壁会影响色彩保真度，其中采用漫反射白色背景的效果最佳。将该流程应用于各类有色水果样本后，可在不同光源条件下实现更为一致的水果色彩还原效果。本流程配套的开源Python软件包（https://github.com/collinswakholi/ColorCorrectionPackage）与交互式用户界面（https://github.com/collinswakholi/ColorCorrectionPackage_UI）已公开上线，可支持可复现分析，并可便捷适配其他受控成像任务。总体而言，该流程可优化数字图像的色彩还原与色彩测量效果，同时有助于弥合高端色彩校正方法与实际常规应用之间的差距。