融合注意力机制的多尺度深度网络的逆半调方法

目的 运用现有的逆半调方法恢复的图像存在着半色调网纹去除不够理想、图像细节恢复不够清晰等问题，为了进一步提高逆半调图像在平滑区域和纹理细节方面的质量，提出一种基于融合注意力机制的多尺度卷积神经网络的逆半调方法。方法 首先，根据半色调图像网点噪声多频分布特点，设计多尺度卷积网络为基础结构的深度学习网络，从多个不同的尺度抑制半色调网纹并恢复不同尺度的图像信息；然后，应用注意力机制重建图像信息，从而生成逆半调图像；最后，提出多任务损失函数加速网络优化，更好地实现逆半调。结果 实验结果表明，运用此方法得到的逆半调图像在视觉上与原始图像更为相近，恢复出的图像细节更好；在客观评价方面，通过与现有的最先进的方法相比，峰值信噪比平均值提高了0.562～10.095 dB，结构相似度平均值提高了0.01～0.171。结论 该方法可以实现半色调图像的高质量恢复。

A Multi-scale Deep Network Combined with Attention Mechanism for Inverse Halftoning

The restored images by the existing inverse halftoning methods still suffer either halftone artifacts or fine detail losses. The paper aims to improve the quality of inverse halftone image in smooth area and texture detail and propose a method of inverse halftoning based on multi-scale convolutional neural network combined with attention mechanism. Firstly, according to the multi-frequency distribution of halftone artifacts, a deep learning network based on multi-scale convolutional neural network is designed to suppress the noise and restore the image information at different scales. Secondly, inverse halftone images are generated by fusing different reconstructed information with attention mechanism. Finally, multi-task loss functions are presented to accelerate network optimization. Experimental results show that the inverse halftone images obtained by the proposed method are more similar to the original images and the image details are recovered better in vision. In terms of objective evaluation, the restored images by the proposed method have significantly higher peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) values compared to the state-of-the-art methods. The average PSNR increased by 0.562-10.95 dB, and the average SSIM increased by 0.01-0.171. This method can achieve high quality restoration of halftone images.