改进3D薄板样条法，提升图像色彩一致性与处理效率
Image color consistency in datasets: the Smooth-TPS3D method
- 引入平滑版TPS3D算法，结合色卡进行后处理校正
- 错误场景率从11%-15%降至不足1%，速度提升20%
- 适合需要高精度色彩一致性的数据集构建者
图像色彩一致性是数字成像数据集构建中的关键问题。本文提出一种改进的3D薄板样条（TPS3D）色彩校正方法，结合色卡（如Macbeth ColorChecker）或其他可读模式，通过后处理实现图像一致性。我们在基于Gehler ColorChecker数据集的增强数据集上，对本方法及其前代版本和其他现有方法进行了基准测试，评估指标包括校正后图像与真实值的接近程度及运行速度。结果表明，TPS3D是最优候选方案；此外，Smooth-TPS3D在性能上等同于原方法，但将此前方法中11%-15%的病态情形失败率降至不到1%，且处理速度比原方法快20%。最后，我们讨论了不同方法在质量、准确性和计算开销之间的权衡。
原文摘要 · Abstract (English)
Image color consistency is the key problem in digital imaging consistency when creating datasets. Here, we propose an improved 3D Thin-Plate Splines (TPS3D) color correction method to be used, in conjunction with color charts (i.e. Macbeth ColorChecker) or other machine-readable patterns, to achieve image consistency by post-processing. Also, we benchmark our method against its former implementation and the alternative methods reported to date with an augmented dataset based on the Gehler's ColorChecker dataset. Benchmark includes how corrected images resemble the ground-truth images and how fast these implementations are. Results demonstrate that the TPS3D is the best candidate to achieve image consistency. Furthermore, our Smooth-TPS3D method shows equivalent results compared to the original method and reduced the 11-15% of ill-conditioned scenarios which the previous method failed to less than 1%. Moreover, we demonstrate that the Smooth-TPS method is 20% faster than the original method. Finally, we discuss how different methods offer different compromises between quality, correction accuracy and computational load.
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