Due to the huge category number, the sophisticated combinations of various
strokes and radicals, and the free writing or printing styles, generating
Chinese characters with diverse styles is always considered as a difficult
task. In this paper, an efficient and generalized deep framework, namely, the
W-Net, is introduced for the one-shot arbitrary-style Chinese character
generation task. Specifically, given a single character (one-shot) with a
specific style (e.g., a printed font or hand-writing style), the proposed W-Net
model is capable of learning and generating any arbitrary characters sharing
the style similar to the given single character. Such appealing property was
rarely seen in the literature. We have compared the proposed W-Net framework to
many other competitive methods. Experimental results showed the proposed method
is significantly superior in the one-shot setting.

通过引入 W-Net 模型，本文提出了一种高效和广义的深度框架，可用于一次过任意风格的汉字生成任务。实验结果表明，该方法在一次生成的设置下明显优于其他竞争方法。

W-Net：基于深度神经网络的一次成像任意风格汉字生成

W-Net: One-Shot Arbitrary-Style Chinese Character Generation with Deep  Neural Networks

Face Super-Resolution (FSR) aims to recover high-resolution (HR) face images
from low-resolution (LR) ones. Despite the progress made by convolutional
neural networks in FSR, the results of existing approaches are not ideal due to
their low reconstruction efficiency and insufficient utilization of prior
information. Considering that faces are highly structured objects, effectively
leveraging facial priors to improve FSR results is a worthwhile endeavor. This
paper proposes a novel network architecture called W-Net to address this
challenge. W-Net leverages meticulously designed Parsing Block to fully exploit
the resolution potential of LR image. We use this parsing map as an attention
prior, effectively integrating information from both the parsing map and LR
images. Simultaneously, we perform multiple fusions in various dimensions
through the W-shaped network structure combined with the LPF(LR-Parsing Map
Fusion Module). Additionally, we utilize a facial parsing graph as a mask,
assigning different weights and loss functions to key facial areas to balance
the performance of our reconstructed facial images between perceptual quality
and pixel accuracy. We conducted extensive comparative experiments, not only
limited to conventional facial super-resolution metrics but also extending to
downstream tasks such as facial recognition and facial keypoint detection. The
experiments demonstrate that W-Net exhibits outstanding performance in
quantitative metrics, visual quality, and downstream tasks.

通过利用面部先验信息，提出了一种名为 W-Net 的新型网络架构，用于解决面部超分辨率问题，该架构充分利用了低分辨率图像的分辨率潜力，并通过使用预测图和低分辨率图像的融合模块实现多维度的融合，同时平衡了感知质量和像素准确性。实验证明 W-Net 在定量指标、视觉质量和下游任务方面表现出色。

W-Net：一种面部特征引导的人脸超分辨网络

W-Net: A Facial Feature-Guided Face Super-Resolution Network

The U-net is a deep-learning network model that has been used to solve a
number of inverse problems. In this work, the concatenation of two-element
U-nets, termed the W-net, operating in k-space (K) and image (I) domains, were
evaluated for multi-channel magnetic resonance (MR) image reconstruction. The
two element network combinations were evaluated for the four possible
image-k-space domain configurations: a) W-net II, b) W-net KK, c) W-net IK, and
d) W-net KI were evaluated. Selected promising four element networks (WW-nets)
were also examined. Two configurations of each network were compared: 1) Each
coil channel processed independently, and 2) all channels processed
simultaneously. One hundred and eleven volumetric, T1-weighted, 12-channel coil
k-space datasets were used in the experiments. Normalized root mean squared
error, peak signal to noise ratio, visual information fidelity and visual
inspection were used to assess the reconstructed images against the fully
sampled reference images. Our results indicated that networks that operate
solely in the image domain are better suited when processing individual
channels of multi-channel data independently. Dual domain methods are more
advantageous when simultaneously reconstructing all channels of multi-channel
data. Also, the appropriate cascade of U-nets compared favorably (p < 0.01) to
the previously published, state-of-the-art Deep Cascade model in in three out
of four experiments.

本研究基于 U-net 深度学习网络模型，将两个元素串联在 k-space 和图像域，考察了多通道 MRI 图像重建。实验结果表明，对于独立通道的重建，仅图像域的网络更好；对于多通道的同时重建，使用双域方法更优。同时发现，与先前发布的 Deep Cascade 模型相比，网络的级联使用在三个实验中有优势（p < 0.01）。