Computed Tomography (CT) is a prominent example of Imaging Inverse Problem
(IIP), highlighting the unrivalled performances of data-driven methods in
degraded measurements setups like sparse X-ray projections. Although a
significant proportion of deep learning approaches benefit from large
supervised datasets to directly map experimental measurements to medical scans,
they cannot generalize to unknown acquisition setups. In contrast, fully
unsupervised techniques, most notably using score-based generative models, have
recently demonstrated similar or better performances compared to supervised
approaches to solve IIPs while being flexible at test time regarding the
imaging setup. However, their use cases are limited by two factors: (a) they
need considerable amounts of training data to have good generalization
properties and (b) they require a backward operator, like
Filtered-Back-Projection in the case of CT, to condition the learned prior
distribution of medical scans to experimental measurements. To overcome these
issues, we propose an unsupervised conditional approach to the Generative
Latent Optimization framework (cGLO), in which the parameters of a decoder
network are initialized on an unsupervised dataset. The decoder is then used
for reconstruction purposes, by performing Generative Latent Optimization with
a loss function directly comparing simulated measurements from proposed
reconstructions to experimental measurements. The resulting approach, tested on
sparse-view CT using multiple training dataset sizes, demonstrates better
reconstruction quality compared to state-of-the-art score-based strategies in
most data regimes and shows an increasing performance advantage for smaller
training datasets and reduced projection angles. Furthermore, cGLO does not
require any backward operator and could expand use cases even to non-linear
IIPs.

在稀疏投影 CT 上测试，通过与现有的基于分数的策略相比，使用多个训练数据集大小，cGLO 提供了更好的重建质量以及对于较小的训练数据集和减少的投影角度展现出逐渐增加的性能优势。此外，cGLO 不需要任何反向操作，甚至可以拓展应用于非线性的 IIPs。

通过生成性潜在优化解决图像反问题

Conditioning Generative Latent Optimization to solve Imaging Inverse  Problems

Generative Adversarial Networks (GANs) have achieved remarkable results in
the task of generating realistic natural images. In most successful
applications, GAN models share two common aspects: solving a challenging saddle
point optimization problem, interpreted as an adversarial game between a
generator and a discriminator functions; and parameterizing the generator and
the discriminator as deep convolutional neural networks. The goal of this paper
is to disentangle the contribution of these two factors to the success of GANs.
In particular, we introduce Generative Latent Optimization (GLO), a framework
to train deep convolutional generators using simple reconstruction losses.
Throughout a variety of experiments, we show that GLO enjoys many of the
desirable properties of GANs: synthesizing visually-appealing samples,
interpolating meaningfully between samples, and performing linear arithmetic
with noise vectors; all of this without the adversarial optimization scheme.

本文介绍了生成对抗网络的成功因素，并提出 “生成潜在优化” 框架来训练深度卷积生成器，实现了可视化合成、样本插值等功能，同时不需要对抗优化方案。