Layer decomposition to separate an input image into base and detail layers has been steadily used for image restoration. Existing residual networks based on an additive model require residual layers with a small output range for fast convergence and visual quality improvement. However, in inverse halftoning, homogenous dot patterns hinder a small output range from the residual layers. Therefore, a new layer decomposition network based on the Gaussian convolution model (GCM) and structure-aware deblurring strategy is presented to achieve residual learning for both the base and detail layers. For the base layer, a new GCM-based residual subnetwork is presented. The GCM utilizes a statistical distribution, in which the image difference between a blurred continuous-tone image and a blurred halftoned image with a Gaussian filter can result in a narrow output range. Subsequently, the GCM-based residual subnetwork uses a Gaussian-filtered halftoned image as input and outputs the image difference as residual, thereby generating the base layer, i.e., the Gaussian-blurred continuous-tone image. For the detail layer, a new structure-aware residual deblurring subnetwork (SARDS) is presented. To remove the Gaussian blurring of the base layer, the SARDS uses the predicted base layer as input and outputs the deblurred version. To more effectively restore image structures such as lines and texts, a new image structure map predictor is incorporated into the deblurring network to induce structure-adaptive learning. This paper provides a method to realize the residual learning of both the base and detail layers based on the GCM and SARDS. In addition, it is verified that the proposed method surpasses state-of-the-art methods based on U-Net, direct deblurring networks, and progressively residual networks.

翻译：将输入图像分解成基层和详细层的图层分解图层已被稳步地用于图像恢复。基于添加模型的现有残余网络需要留置层,其输出范围较小,以便快速趋同和视觉质量改进。然而,在反半调中,同质点模式阻碍着残余层的较小输出范围。因此,基于高西亚混进模型(GCM)和结构分解战略的新的层分解网络正在推出,以在基层和详细层中实现剩余学习。对于基层,将新的基于GCM的连续子网络显示为一个新的基层。GCM使用统计分布层,其中图像在模糊的连续图层图像和带有高氏过滤过滤过滤器的模糊半调图像之间有差异,因此,基于GCM的残余网络使用高频过滤半调半调图像网络作为输入和输出,从而生成基层的图层。例如,Gaussion-bred-连续图层图像网络使用统计分布图层图层结构的清晰度,在SAR-SAR-Sal 数据库中,将数据库的底层结构进行新的再化。