In multiple image mosaicking, color inconsistency is a common and severe problem that degrades the quality of composite images. To avoid the appearance of visible seams, we need to optimize the color consistency before mosaicking multiple images. To facilitate the global optimization framework, existing approaches mainly use less flexible global models, e.g., the linear or gamma model, to eliminate the color differences between multiple images. These models can effectively ensure that the global tones in multiple images are consistent. However, their ability to correct local color discrepancies is usually poor. In this paper, we propose a novel image color correction approach that can correct global and local color discrepancies simultaneously and preserve image gradient as much as possible. First, we design an effective and flexible color correction model to represent the color mapping function for each image. Instead of using the same global model to correct color discrepancies for all pixels in one image, we apply a series of local linear models for color correction. For different superpixel regions, different linear models are applied to model the mapping functions. Second, based on this model, a specific global cost function that considers both gradient preservation and color consistency is designed and solved. In addition, a global color constraint is fused into this cost function to ensure that the corrected images have the similar global tone. Thus, we can jointly optimize the global and local color consistency by minimizing this cost function. The experimental results on several challenging datasets captured by different sensors demonstrate that the proposed approach outperforms the state-of-the-art color correction approaches in both visual quality and quantitative metrics.

在多图像拼接中，颜色不一致是一个普遍而严重的问题，它降低了合成图像的质量。为了避免出现可见的接缝，我们需要在镶嵌多个图像之前优化颜色一致性。为了促进全局优化框架，现有方法主要使用不太灵活的全局模型，例如线性或伽马模型，以消除多个图像之间的色差。这些模型可以有效地确保多个图像中的全局色调一致。但是，它们校正局部颜色差异的能力通常很差。在本文中，我们提出了一种新颖的图像颜色校正方法，该方法可以同时校正全局和局部颜色差异，并尽可能保留图像梯度。第一，我们设计了一种有效且灵活的颜色校正模型，以表示每个图像的颜色映射功能。我们没有使用相同的全局模型来校正一个图像中所有像素的颜色差异，而是应用了一系列局部线性模型来进行颜色校正。对于不同的超像素区域，将应用不同的线性模型来对映射函数进行建模。其次，基于此模型，设计并解决了同时考虑了梯度保留和颜色一致性的特定全局成本函数。另外，全局颜色约束被融合到该成本函数中，以确保校正后的图像具有相似的全局色调。因此，我们可以通过最小化此成本函数来共同优化全局和局部颜色一致性。