论文标题
基于高斯混合物建模的示例色彩转移
Example-based Color Transfer with Gaussian Mixture Modeling
论文作者
论文摘要
色彩转移在图像编辑中起着关键作用，最近引起了引人注目的关注。由于各种问题，例如耗时的手动调整和先前的细分问题，这仍然是迄今为止的挑战。在本文中，我们建议在概率框架下建模色转移，并将其作为参数估计问题。特别是，我们将传递的图像与高斯混合模型（GMM）下的示例图像联系起来，并将传输的图像颜色视为GMM质心。我们采用预期最大化（EM）算法（E-step和M-Step）进行优化。为了更好地保留梯度信息，我们将基于拉普拉斯的正则化项引入了M-STEP的目标函数，该目标通过得出梯度下降算法来解决。鉴于源图像的输入和示例图像，我们的方法能够随着EM迭代的增加而生成连续的色彩转移结果。各种实验表明，我们的方法通常在视觉和定量上都优于其他竞争色转移方法。
Color transfer, which plays a key role in image editing, has attracted noticeable attention recently. It has remained a challenge to date due to various issues such as time-consuming manual adjustments and prior segmentation issues. In this paper, we propose to model color transfer under a probability framework and cast it as a parameter estimation problem. In particular, we relate the transferred image with the example image under the Gaussian Mixture Model (GMM) and regard the transferred image color as the GMM centroids. We employ the Expectation-Maximization (EM) algorithm (E-step and M-step) for optimization. To better preserve gradient information, we introduce a Laplacian based regularization term to the objective function at the M-step which is solved by deriving a gradient descent algorithm. Given the input of a source image and an example image, our method is able to generate continuous color transfer results with increasing EM iterations. Various experiments show that our approach generally outperforms other competitive color transfer methods, both visually and quantitatively.
