In this paper, we propose a fully automatic image colorization method for grayscale images using neural network and optimization. For a determined training set including the gray images and its corresponding color images, our method segments grayscale images into superpixels and then extracts features of particular points of interest in each superpixel. The obtained features and their RGB values are given as input for, the training colorization neural network of each pixel. To achieve a better image colorization effect in shorter running time, our method further propagates the resulting color points to neighboring pixels for improved colorization results. In the propagation of color, we present a cost function to formalize the premise that neighboring pixels should have the maximum positive similarity of intensities and colors; we then propose our solution to solving the optimization problem. At last, a guided image filter is employed to refine the colorized image. Experiments on a wide variety of images show that the proposed algorithms can achieve superior performance over the state-of-the-art algorithms.

中文翻译：

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使用种子点传播对灰度图像进行自动着色

在本文中，我们提出了一种使用神经网络和优化的灰度图像全自动图像着色方法。对于一个确定的训练集，包括灰度图像及其相应的彩色图像，我们的方法将灰度图像分割成超像素，然后在每个超像素中提取特定兴趣点的特征。获得的特征及其 RGB 值作为输入，用于每个像素的训练着色神经网络。为了在更短的运行时间内获得更好的图像着色效果，我们的方法进一步将生成的色点传播到相邻像素以改进着色结果。在颜色的传播中，我们提出了一个代价函数来形式化前提，即相邻像素应该具有最大的强度和颜色的正相似性；然后我们提出我们的解决方案来解决优化问题。最后，使用引导图像过滤器来细化彩色图像。对各种图像的实验表明，所提出的算法可以实现优于最先进算法的性能。