Removing noise from images is a challenging and fundamental problem in the
field of computer vision. Images captured by modern cameras are inevitably
degraded by noise which limits the accuracy of any quantitative measurements on
those images. In this project, we propose a novel image reconstruction
framework which can be used for tasks such as image denoising, deblurring or
inpainting. The model proposed in this project is based on Vision Transformer
(ViT) that takes 2D images as input and outputs embeddings which can be used
for reconstructing denoised images. We incorporate four additional optimization
techniques in the framework to improve the model reconstruction capability,
namely Locality Sensitive Attention (LSA), Shifted Patch Tokenization (SPT),
Rotary Position Embeddings (RoPE) and adversarial loss function inspired from
Generative Adversarial Networks (GANs). LSA, SPT and RoPE enable the
transformer to learn from the dataset more efficiently, while the adversarial
loss function enhances the resolution of the reconstructed images. Based on our
experiments, the proposed architecture outperforms the benchmark U-Net model by
more than 3.5\% structural similarity (SSIM) for the reconstruction tasks of
image denoising and inpainting. The proposed enhancements further show an
improvement of \textasciitilde5\% SSIM over the benchmark for both tasks.

本文提出了一个基于 Vision Transformer (ViT) 的图像重构框架，利用 4 种优化技术和生成对抗网络（GANs）启发的对抗性损失函数，用于图像去噪和修复，实验表明该框架在结构相似性（SSIM）方面比 U-Net 模型高出超过 3.5％，对于这两个任务，提议的增强算法进一步展示了超过基准的	extasciitilde5％SSIM 的改进。

使用增强视觉 Transformer 进行图像重建

Image Reconstruction using Enhanced Vision Transformer

We investigate the power of censoring techniques, first developed for
learning {\em fair representations}, to address domain generalization. We
examine {\em adversarial} censoring techniques for learning invariant
representations from multiple "studies" (or domains), where each study is drawn
according to a distribution on domains. The mapping is used at test time to
classify instances from a new domain. In many contexts, such as medical
forecasting, domain generalization from studies in populous areas (where data
are plentiful), to geographically remote populations (for which no training
data exist) provides fairness of a different flavor, not anticipated in
previous work on algorithmic fairness.
We study an adversarial loss function for $k$ domains and precisely
characterize its limiting behavior as $k$ grows, formalizing and proving the
intuition, backed by experiments, that observing data from a larger number of
domains helps. The limiting results are accompanied by non-asymptotic
learning-theoretic bounds. Furthermore, we obtain sufficient conditions for
good worst-case prediction performance of our algorithm on previously unseen
domains. Finally, we decompose our mappings into two components and provide a
complete characterization of invariance in terms of this decomposition. To our
knowledge, our results provide the first formal guarantees of these kinds for
adversarial invariant domain generalization.

本文研究使用先前为学习 “公平表示” 而开发的审查技术来解决域泛化问题。我们研究了 $k$ 个域的对抗性损失函数及其渐近行为，并在此基础上获得了算法在以前未见过的域上良好最坏情况预测性能的充分条件。我们提供了完整的不变性特征描述，并为敌对不变域泛化提供了第一个形式化保证。

基于不变式学习的对抗训练域不变表示的表示方法

Representation via Representations: Domain Generalization via  Adversarially Learned Invariant Representations

Face hallucination, which is the task of generating a high-resolution face
image from a low-resolution input image, is a well-studied problem that is
useful in widespread application areas. Face hallucination is particularly
challenging when the input face resolution is very low (e.g., 10 x 12 pixels)
and/or the image is captured in an uncontrolled setting with large pose and
illumination variations. In this paper, we revisit the algorithm introduced in
[1] and present a deep interpretation of this framework that achieves
state-of-the-art under such challenging scenarios. In our deep network
architecture the global and local constraints that define a face can be
efficiently modeled and learned end-to-end using training data. Conceptually
our network design can be partitioned into two sub-networks: the first one
implements the holistic face reconstruction according to global constraints,
and the second one enhances face-specific details and enforces local patch
statistics. We optimize the deep network using a new loss function for
super-resolution that combines reconstruction error with a learned face quality
measure in adversarial setting, producing improved visual results. We conduct
extensive experiments in both controlled and uncontrolled setups and show that
our algorithm improves the state of the art both numerically and visually.

本文提出了一种面部图像超分辨网络，该网络在低分辨率、大姿态、大光照变化等困难情况下，通过全局和局部约束的多层次特征提取和重建，通过对抗性损失函数进行优化，来实现面部图像重建和细节增强的目标，同时在控制和不受控制的实验环境下，与现有算法相比，在数量和视觉效果上均获得了显著的提高。