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Automatic Colorization of Images from Chinese Black and White Films Based on CNN

机译:基于CNN的中国黑白电影自动彩色

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The colorization of black and white films was a hot topic in the 1980s. Some black-and-white movies regained their luster through colorization. Although people are controversial about the artistic value of film colorization, it is no doubt that color images can enhance visual effects. Inspired by the recent colorization methods using deep learning, we propose a novel colorization model which combines two Convolutional Neural Networks and uses multi-scale convolution kernels to get better spatial consistency. Most of the current datasets used in the colorization networks are not applicable to colorizing images from Chinese black and white films. The main reason is that the objects in these films are very different from today's. To address this, we extract a large number of images from Chinese color films of the last century as a training dataset. Experiments demonstrate that our model can obtain pretty good results of colorizing images from Chinese black and white films.
机译:黑白电影的着色是20世纪80年代的热门话题。一些黑白电影通过彩色重新恢复光泽。虽然人们对胶片着色的艺术价值有争议,但毫无疑问,彩色图像可以增强视觉效果。灵感来自最近使用深度学习的彩色方法,我们提出了一种新颖的彩色模型,它结合了两个卷积神经网络,并使用多尺度卷积核来获得更好的空间一致性。颜色网络中使用的大多数当前数据集不适用于来自中国黑白电影的着色图像。主要原因是这些电影中的物体与今天的情况非常不同。为了解决这个问题,我们从上个世纪中的中国颜色电影提取了大量图像作为训练数据集。实验表明,我们的模型可以获得来自中国黑白胶片的着色图像的良好结果。

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