Abstract
The primary issue in inverse halftoning is removing noisy dots on flat areas and restoring image structures (e.g., lines, patterns) on textured areas. Hence, a new structure-aware deep convolutional neural network that incorporates two subnetworks is proposed in this paper. One subnetwork is for image structure prediction while the other is for continuous-tone image reconstruction. First, to predict image structures, patch pairs comprising continuous-tone patches and the corresponding halftoned patches generated through digital halftoning are trained. Subsequently, gradient patches are generated by convolving gradient filters with the continuous-tone patches. The subnetwork for the image structure prediction is trained using the mini-batch gradient descent algorithm given the halftoned patches and gradient patches, which are fed into the input and loss layers of the subnetwork, respectively. Next, the predicted map including the image structures is stacked on the top of the input halftoned image through a fusion layer and fed into the image reconstruction subnetwork such that the entire network is trained adaptively to the image structures. The experimental results confirm that the proposed structure-aware network can remove noisy dot-patterns well on flat areas and restore details clearly on textured areas. Furthermore, it is demonstrated that the proposed method surpasses the conventional state-of-the-art methods based on deep convolutional neural networks and locally learned dictionaries.
Abstract (translated)
反向半色调的主要问题是去除平面区域上的噪声点，恢复纹理区域上的图像结构（如线条、图案）。因此，本文提出了一种结合两个子网络的结构感知深卷积神经网络。一个子网络用于图像结构预测，另一个子网络用于连续色调图像重建。首先，为了预测图像结构，训练了由连续色调补丁和由数字半色调生成的相应半色调补丁组成的补丁对。然后，将梯度滤波器与连续色调片卷积，生成梯度片。针对图像结构预测的子网络问题，分别在子网络输入层和损失层中引入半色调和梯度块，采用小批量梯度下降算法对其进行训练。然后，将包含图像结构的预测图通过融合层叠加在输入的半色调图像的顶部，送入图像重建子网络，使整个网络适应图像结构的训练。实验结果表明，该结构感知网络能够很好地去除平坦区域的噪声点图案，并能清晰地恢复纹理区域的细节。并证明了该方法优于传统的基于深度卷积神经网络和局部学习词典的最新方法。
URL
https://arxiv.org/abs/1905.00637
