Image inverse halftoning is a classic image restoration task, aiming to
recover continuous-tone images from halftone images with only bilevel pixels.
Because the halftone images lose much of the original image content, inverse
halftoning is a classic ill-problem. Although existing inverse halftoning
algorithms achieve good performance, their results lose image details and
features. Therefore, it is still a challenge to recover high-quality
continuous-tone images. In this paper, we propose an end-to-end multiscale
progressively residual learning network (MSPRL), which has a UNet architecture
and takes multiscale input images. To make full use of different input image
information, we design a shallow feature extraction module to capture similar
features between images of different scales. We systematically study the
performance of different methods and compare them with our proposed method. In
addition, we employ different training strategies to optimize the model, which
is important for optimizing the training process and improving performance.
Extensive experiments demonstrate that our MSPRL model obtains considerable
performance gains in detail restoration.

本研究提出了一种端到端多尺度渐进式残差学习网络（MSPRL）进行图像反半色调，采用 UNet 架构和多尺度输入图像来恢复高质量的连续色调图像，并在不同的训练策略下进行了比较，结果表明 MSPRL 处理细节恢复问题中具备显著的性能优势。

重新考虑 PRL: 用于反半色调的多尺度渐进残差学习网络

Rethinking PRL: A Multiscale Progressively Residual Learning Network for  Inverse Halftoning

Joint learning of super-resolution (SR) and inverse tone-mapping (ITM) has
been explored recently, to convert legacy low resolution (LR) standard dynamic
range (SDR) videos to high resolution (HR) high dynamic range (HDR) videos for
the growing need of UHD HDR TV/broadcasting applications. However, previous
CNN-based methods directly reconstruct the HR HDR frames from LR SDR frames,
and are only trained with a simple L2 loss. In this paper, we take a
divide-and-conquer approach in designing a novel GAN-based joint SR-ITM
network, called JSI-GAN, which is composed of three task-specific subnets: an
image reconstruction subnet, a detail restoration (DR) subnet and a local
contrast enhancement (LCE) subnet. We delicately design these subnets so that
they are appropriately trained for the intended purpose, learning a pair of
pixel-wise 1D separable filters via the DR subnet for detail restoration and a
pixel-wise 2D local filter by the LCE subnet for contrast enhancement.
Moreover, to train the JSI-GAN effectively, we propose a novel detail GAN loss
alongside the conventional GAN loss, which helps enhancing both local details
and contrasts to reconstruct high quality HR HDR results. When all subnets are
jointly trained well, the predicted HR HDR results of higher quality are
obtained with at least 0.41 dB gain in PSNR over those generated by the
previous methods.

本文提出了一种基于生成对抗网络的分组超分辨率和反色调映射联合学习模型，通过精心设计包括图像重建子网、细节恢复子网和局部对比度增强子网等三个任务特定子网，训练一对 1D 可分离细节恢复像素滤波器和一个局部对比度增强 2D 滤波器，以提高本文模型处理 HDR 图像过程中所需的超分辨率和反色调映射性能，实验证明该模型能够比之前的方法提高 0.41 dB 的 PSNR 值。