Semantic inpainting or image completion alludes to the task of inferring
arbitrary large missing regions in images based on image semantics. Since the
prediction of image pixels requires an indication of high-level context, this
makes it significantly tougher than image completion, which is often more
concerned with correcting data corruption and removing entire objects from the
input image. On the other hand, image enhancement attempts to eliminate
unwanted noise and blur from the image, along with sustaining most of the image
details. Efficient image completion and enhancement model should be able to
recover the corrupted and masked regions in images and then refine the image
further to increase the quality of the output image. Generative Adversarial
Networks (GAN), have turned out to be helpful in picture completion tasks. In
this chapter, we will discuss the underlying GAN architecture and how they can
be used used for image completion tasks.

语义修复，图像完成，图像增强，生成对抗网络 (GAN)，图像质量。

使用生成对抗网络的语义图像补全与增强

Semantic Image Completion and Enhancement using GANs

Traditional convolution-based generative adversarial networks synthesize
images based on hierarchical local operations, where long-range dependency
relation is implicitly modeled with a Markov chain. It is still not sufficient
for categories with complicated structures. In this paper, we characterize
long-range dependence with attentive normalization (AN), which is an extension
to traditional instance normalization. Specifically, the input feature map is
softly divided into several regions based on its internal semantic similarity,
which are respectively normalized. It enhances consistency between distant
regions with semantic correspondence. Compared with self-attention GAN, our
attentive normalization does not need to measure the correlation of all
locations, and thus can be directly applied to large-size feature maps without
much computational burden. Extensive experiments on class-conditional image
generation and semantic inpainting verify the efficacy of our proposed module.

本研究提出了注意力归一化模块 (AN) 用于改进图像的长程关联性以及利用语义对分割特征图进行处理，相比于自注意力 GAN，AN 不需要测量所有位置之间的关联性，因此可以直接应用于大尺寸的特征图，本文通过对比实验证明了 AN 的有效性。

条件图像生成的注意力归一化

Attentive Normalization for Conditional Image Generation

A fundamental problem in geostatistical modeling is to infer the
heterogeneous geological field based on limited measurements and some prior
spatial statistics. Semantic inpainting, a technique for image processing using
deep generative models, has been recently applied for this purpose,
demonstrating its effectiveness in dealing with complex spatial patterns.
However, the original semantic inpainting framework incorporates only
information from direct measurements, while in geostatistics indirect
measurements are often plentiful. To overcome this limitation, here we propose
a physics-informed semantic inpainting framework, employing the Wasserstein
Generative Adversarial Network with Gradient Penalty (WGAN-GP) and jointly
incorporating the direct and indirect measurements by exploiting the underlying
physical laws. Our simulation results for a high-dimensional problem with 512
dimensions show that in the new method, the physical conservation laws are
satisfied and contribute in enhancing the inpainting performance compared to
using only the direct measurements.

提出了一种基于物理信息的图像修复方法，应用于地质统计学建模中，结合直接和间接测量，使用 Wasserstein 生成对抗网络，并验证了物理守恒定律在提高图像修复性能方面的作用。

物理信息语义修复：地质统计建模应用

Physics-informed semantic inpainting: Application to geostatistical  modeling

Semantic inpainting is the task of inferring missing pixels in an image given
surrounding pixels and high level image semantics. Most semantic inpainting
algorithms are deterministic: given an image with missing regions, a single
inpainted image is generated. However, there are often several plausible
inpaintings for a given missing region. In this paper, we propose a method to
perform probabilistic semantic inpainting by building a model, based on
PixelCNNs, that learns a distribution of images conditioned on a subset of
visible pixels. Experiments on the MNIST and CelebA datasets show that our
method produces diverse and realistic inpaintings.

该研究论文提出了一种基于 PixelCNNs 的概率语义修复方法，该方法可以学习有关给定少量像素的图像分布，从而产生多样化和逼真的修复结果，实验证明该方法在 MNIST 和 CelebA 数据集上的表现。