Image learning and colorization are hot spots in multimedia domain. Inspired by the learning capability of humans, in this paper, we propose an automatic colorization method with a learning framework. This method can be viewed as a hybrid of exemplar-based and learning-based method, and it decouples the colorization process and learning process so as to generate various color styles for the same gray image. The matching process in the exemplar-based colorization method can be regarded as a parameterized function, and we employ a large amount of color images as the training samples to fit the parameters. During the training process, the color images are the ground truths, and we learn the optimal parameters for the matching process by minimizing the errors in terms of the parameters for the matching function. To deal with images with various compositions, a global feature is introduced, which can be used to classify the images with respect to their compositions, and then learn the optimal matching parameters for each image category individually. What's more, a spatial consistency based post-processing is design to smooth the extracted color information from the reference image to remove matching errors. Extensive experiments are conducted to verify the effectiveness of the method, and it achieves comparable performance against the state-of-the-art colorization algorithms.
翻译:图像学习和色彩化是多媒体域的热点。 受人类学习能力的启发, 我们在本文件中建议了一种带有学习框架的自动色彩化方法。 这种方法可以被视为以实例为基础的和以学习为基础的方法的混合体, 并且可以分离颜色化过程和学习过程, 以便产生相同灰色图像的各种颜色样式。 以实例为基础的色彩化方法中的匹配过程可以被视为一个参数化功能, 我们使用大量彩色图像作为培训样本来匹配参数。 在培训过程中, 彩色图像是地面的真相, 我们通过尽量减少匹配函数参数中的错误来学习匹配进程的最佳参数。 为了处理各种构造中的图像, 引入了一个全球特征, 用于对图像的构成进行分类, 然后为每个图像类别单独学习最佳匹配参数。 更重要的是, 基于空间的图像处理是设计从引用图像中提取的颜色信息以清除匹配错误。 广泛的彩色化实验将对照其匹配性能进行对比。 彩色化方法的性能测试, 与州化方法的性能。