基于分类字典的多内容灰度图像彩色化算法

为解决基于字典学习与稀疏表示的灰度图像彩色化算法只对单一内容图像有效这一问题,提出了一种新型的图像彩色化方法. 首先,根据目标灰度图像的子内容分别选取多组参考彩色图像,从各组参考彩色图像中选取对应子内容的样本图像块;然后,分别进行字典训练,得到基于内容的分类字典;最后,根据重建误差最小化原则,查找最佳匹配字典,进而实现灰度图像的彩色化. 该算法是一种自动算法,在保证图像彩色化过程自动化的前提下,提高了彩色化效果. 实验结果表明:该算法能够对目标灰度图像中的不同内容分别进行正确彩色化处理.

Multiple-content Image Colorization Algorithm Based on Classified Dictionary

A novel method of image colorization is proposed. It overcomes the problem existed in algorithm of image colorization based on dictionary learning and sparse representation. First, multiple blocks from reference color images was selected according to the sub-contents of target gray-scale image. Second, the multiple blocks are trained respectively to obtain classified dictionaries based on contents. Finally, the best matching sub-dictionary can be decided by minimizing the reconstruction error and the gray-scale image is colorized. This algorithm was automatic and the performance of colorization was improved on the premise of guaranteeing the automation of the image colorization process. Experimental results show that the algorithm is able to colorize the target gray-scale images of different contents automatically.