Numerous methods have been proposed to transform color and grayscale images to their single bit-per-pixel binary counterparts. Commonly, the goal is to enhance specific attributes of the original image to make it more amenable for analysis. However, when the resulting binarized image is intended for human viewing, aesthetics must also be considered. Binarization techniques, such as half-toning, stippling, and hatching, have been widely used for modeling the original image's intensity profile. We present an automated method to transform an image to a set of binary textures that represent not only the intensities, but also the colors of the original. The foundation of our method is information preservation: creating a set of textures that allows for the reconstruction of the original image's colors solely from the binarized representation. We present techniques to ensure that the textures created are not visually distracting, preserve the intensity profile of the images, and are natural in that they map sets of colors that are perceptually similar to patterns that are similar. The approach uses deep-neural networks and is entirely self-supervised; no examples of good vs. bad binarizations are required. The system yields aesthetically pleasing binary images when tested on a variety of image sources.

中文翻译：

---

颜色纹理：使用可变位深度纹理的颜色自然表示

已经提出了许多方法来将彩色和灰度图像转换为它们的每像素单个比特的二进制对应物。通常，目标是增强原始图像的特定属性，使其更易于分析。但是，当将生成的二值化图像用于人类观看时，还必须考虑美观性。二值化技术（例如半色调，点画和阴影）已广泛用于对原始图像的强度分布进行建模。我们提出了一种自动方法，可以将图像转换为一组二进制纹理，这些纹理不仅代表强度，还代表原始颜色。我们方法的基础是信息保存：创建一组纹理，仅从二值化表示形式中就可以重建原始图像的颜色。我们提出的技术可确保创建的纹理不会在视觉上分散注意力，保留图像的强度分布并且是自然的，因为它们可以映射在视觉上与相似图案相似的颜色集。该方法使用深层神经网络，并且完全是自我监督的。不需要好的和坏的二值化的例子。在各种图像源上进行测试时，该系统可产生美观的二进制图像。