In this paper, a learning-free color constancy algorithm called the Patch-wise Bright Pixels (PBP) is proposed. In this algorithm, an input image is first downsampled and then cut equally into a few patches. After that, according to the modified brightness of each patch, a proper fraction of brightest pixels in the patch is selected. Finally, Gray World (GW)-based methods are applied to the selected bright pixels to estimate the illuminant of the scene. Experiments on NUS $8$-Camera Dataset show that the PBP algorithm outperforms the state-of-the-art learning-free methods as well as a broad range of learning-based ones. In particular, PBP processes a $1080$p image within two milliseconds, which is hundreds of times faster than the existing learning-free ones. Our algorithm offers a potential solution to the full-screen smart phones whose screen-to-body ratio is $100$\%.
翻译:在本文中, 提出了一个名为 Ppatch- Wisy Bright 像素( PBP) 的无学习色彩凝固算法。 在这个算法中, 输入图像首先被下标, 然后被同样切成几个补丁 。 在此之后, 根据每个补丁的修改亮度, 选中了补丁中最亮的像素的适当部分 。 最后, 灰色世界( GW) 方法被应用到选中的亮点像素中来估计场景的亮点 。 在 NUS $8$- Camera 数据集上进行的实验显示, PBP 算法超过了最先进的无学习方法以及广泛的学习基础方法 。 特别是, PBP 在两毫秒内处理一个 1080 美元 的图像, 比现有的无学习的像素要快数百倍。 我们的算法为全屏智能手机提供了潜在的解决方案, 他们的屏幕对机比100美元。