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首页> 外文期刊>ACM Transactions on Graphics >Deep Context-Aware Descreening and Rescreening of Halftone Images
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Deep Context-Aware Descreening and Rescreening of Halftone Images

机译：对半色调图像进行深度上下文感知的去网纹和重新网纹

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摘要

A fully automatic method for descreening halftone images is presented based on convolutional neural networks with end-to-end learning. Incorporating context level information, the proposed method not only removes halftone artifacts but also synthesizes the fine details lost during halftone. The method consists of two main stages. In the first stage, intrinsic features of the scene are extracted, the low-frequency reconstruction of the image is estimated, and halftone patterns are removed. For the intrinsic features, the edges and object-categories are estimated and fed to the next stage as strong visual and contextual cues. In the second stage, fine details are synthesized on top of the low-frequency output based on an adversarial generative model. In addition, the novel problem of rescreening is addressed, where a natural input image is halftoned so as to be similar to a separately given reference halftone image. To this end, a two-stage convolutional neural network is also presented. Both networks are trained with millions of before-and-after example image pairs of various halftone styles. Qualitative and quantitative evaluations are provided, which demonstrates the effectiveness of the proposed methods.
机译：提出了一种基于卷积神经网络的端到端学习的全自动半色调图像去网方法。结合上下文级别信息，所提出的方法不仅去除了半色调伪像，而且还合成了在半色调期间丢失的精细细节。该方法包括两个主要阶段。在第一阶段，提取场景的固有特征，估计图像的低频重建，并去除半色调图案。对于内在特征，将对边缘和对象类别进行估计，并将其作为强烈的视觉和上下文提示提供给下一阶段。在第二阶段，基于对抗性生成模型，在低频输出的顶部合成精细的细节。另外，解决了重新筛查的新颖问题，其中自然输入图像被半色调化以类似于单独给出的参考半色调图像。为此，还提出了两阶段卷积神经网络。这两个网络都接受了数以百万计的各种半色调样式的前后示例图像对的训练。提供了定性和定量评估，证明了所提出方法的有效性。

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