Traditional halftoning usually drops colors when dithering images with binary dots, which makes it difficult to recover the original color information. We proposed a novel halftoning technique that converts a color image into a binary halftone with full restorability to its original version. Our novel base halftoning technique consists of two convolutional neural networks (CNNs) to produce the reversible halftone patterns, and a noise incentive block (NIB) to mitigate the flatness degradation issue of CNNs. Furthermore, to tackle the conflicts between the blue-noise quality and restoration accuracy in our novel base method, we proposed a predictor-embedded approach to offload predictable information from the network, which in our case is the luminance information resembling from the halftone pattern. Such an approach allows the network to gain more flexibility to produce halftones with better blue-noise quality without compromising the restoration quality. Detailed studies on the multiple-stage training method and loss weightings have been conducted. We have compared our predictor-embedded method and our novel method regarding spectrum analysis on halftone, halftone accuracy, restoration accuracy, and the data embedding studies. Our entropy evaluation evidences our halftone contains less encoding information than our novel base method. The experiments show our predictor-embedded method gains more flexibility to improve the blue-noise quality of halftones and maintains a comparable restoration quality with a higher tolerance for disturbances.

翻译：传统半色调技术在利用二值点进行抖动时通常会丢失颜色信息，这使得恢复原始色彩信息变得困难。我们提出了一种新型半色调技术，可将彩色图像转换为具有完全可恢复性的二值半色调图像。该基础半色调技术包含两个卷积神经网络（CNN）用于生成可逆半色调图案，以及一个噪声激励模块（NIB）以缓解CNN的平坦化退化问题。此外，为解决基础方法中蓝噪声质量与恢复精度之间的冲突，我们提出了一种预测器嵌入方法，将可预测信息（本例中为从半色调图案重构的亮度信息）从网络中卸载。这种方法使网络在保持恢复质量的同时，获得更大灵活性以生成具有更优蓝噪声质量的半色调图像。我们详细研究了多阶段训练方法和损失权重策略，并从半色调频谱分析、半色调精度、恢复精度及数据嵌入研究等方面对预测器嵌入方法与基础方法进行了比较。熵值评估表明，我们的半色调图像包含的编码信息少于基础方法。实验证明，我们的预测器嵌入方法在保持可比恢复质量的同时，能更灵活地提升半色调的蓝噪声质量，并具有更高的抗干扰容限。