Mutimbu Lawrence, Robles-Kelly Antonio
IEEE Trans Image Process. 2016 Nov;25(11):5383-5396. doi: 10.1109/TIP.2016.2605003. Epub 2016 Aug 31.
This paper presents a method to recover a spatially varying illuminant color estimate from scenes lit by multiple light sources. Starting with the image formation process, we formulate the illuminant recovery problem in a statistically data-driven setting. To do this, we use a factor graph defined across the scale space of the input image. In the graph, we utilize a set of illuminant prototypes computed using a data driven approach. As a result, our method delivers a pixelwise illuminant color estimate being devoid of libraries or user input. The use of a factor graph also allows for the illuminant estimates to be recovered making use of a maximum a posteriori inference process. Moreover, we compute the probability marginals by performing a Delaunay triangulation on our factor graph. We illustrate the utility of our method for pixelwise illuminant color recovery on widely available data sets and compare against a number of alternatives. We also show sample color correction results on real-world images.
本文提出了一种从由多个光源照亮的场景中恢复空间变化的光源颜色估计的方法。从图像形成过程开始，我们在统计数据驱动的设置中制定光源恢复问题。为此，我们使用在输入图像的尺度空间上定义的因子图。在图中，我们利用一组使用数据驱动方法计算的光源原型。结果，我们的方法提供了一个无需库或用户输入的逐像素光源颜色估计。因子图的使用还允许利用最大后验推理过程来恢复光源估计。此外，我们通过在因子图上执行德劳内三角剖分来计算概率边际。我们在广泛可用的数据集上说明了我们的方法用于逐像素光源颜色恢复的效用，并与许多替代方法进行了比较。我们还展示了在真实世界图像上的样本颜色校正结果。
