Image color consistency in datasets: the Smooth-TPS3D method

TL;DR

This paper introduces the Smooth-TPS3D method, an improved 3D Thin-Plate Splines approach for achieving consistent image color correction in datasets, demonstrating superior accuracy and efficiency over previous methods.

Contribution

The paper presents the Smooth-TPS3D method, enhancing color correction accuracy and computational speed, and benchmarks it against existing techniques using an augmented dataset.

Findings

Smooth-TPS3D reduces ill-conditioned scenarios from 11-15% to less than 1%.

The method is 20% faster than the original TPS3D.

It achieves comparable or better color correction accuracy compared to previous methods.

Abstract

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.