局部线性模型优化的灰度图像彩色化

提出一种有效的仅需要少量线条着色的灰度图像彩色化算法。该算法在Lab颜色空间实现,基于抠图拉普拉斯矩阵设计了一个局部线性优化模型,只需要少量的人工线条着色就能产生高质量的彩色化图像。该模型的彩色化效果总体上与现有方法相当,而在某些情况下,能降低在灰度图像彩色化过程中出现的色彩渗透问题。局部线性优化模型建立的代价函数最优解能通过求解稀疏线性方程组获得。在构建抠图拉普拉斯矩阵时,发现利用扩散距离来代替欧氏距离能对本文模型进一步改进。实验结果显示,用基于扩散距离的改进局部线性优化模型方法,和基于欧氏距离的局部线性优化模型算法相比较,在减少人工线条交互和彩色化效果方面都能有较好的改进。

Local linear model optimization based grayscale image colorization

An effective grayscale image colorization technique is presented in this paper by annotating the image with a few color scribbles. A cost function from a local linear model optimization assumption on Lab color channels is designed and derived. By taking advantage of the matting Laplacian matrix, the local linear model optimization can produce high quality colorizations as existing methods, while having better performance in color bleeding with sparse constraints. Our local linear model optimization is actually the global optimum of the cost function, which can be solved with a sparse linear system. We further improve the performance of our primary model to use diffusion distances instead of Euclidean distances for the construction of the matting Laplacian matrix. The experimental results show that fewer scribbles are required and better colorizations are produced with the improved diffusion distances based optimization model.