驯服可逆半调色：基于预测亮度的方法
Taming Reversible Halftoning via Predictive Luminance
AI总结：
提出一种基于预测亮度的可逆半调色技术，通过双CNN和噪声激励模块生成可逆半调色图案，并利用预测器嵌入方法卸载亮度信息，以提升蓝噪声质量并保持恢复精度。
摘要：
传统半调色在将图像二值化为点阵时通常会丢失颜色信息，这使得恢复原始颜色信息变得困难。我们提出了一种新颖的半调色技术，能够将彩色图像转换为具有完全可恢复性的二值半调色图像，使其能恢复至原始版本。我们新颖的基础半调色技术由两个卷积神经网络（CNN）组成，用于生成可逆的半调色图案，并引入一个噪声激励模块（NIB）来缓解CNN的平坦度退化问题。此外，为了解决我们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，将可预测的信息（在本例中为从半调色图案中推断出的亮度信息）从网络中卸载。这种方法使网络获得更大的灵活性，能够在不影响恢复质量的前提下生成具有更好蓝噪声质量的半调色图像。我们对多阶段训练方法和损失权重进行了详细研究。我们通过半调色频谱分析、半调色精度、恢复精度以及数据嵌入研究，比较了我们的预测器嵌入方法和我们新颖的基础方法。我们的熵评估表明，我们的半调色图像比基础方法包含更少的编码信息。实验表明，我们的预测器嵌入方法获得了更大的灵活性来改善半调色的蓝噪声质量，并保持了相当的恢复质量，且对干扰具有更高的容忍度。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
