Image enhancement holds extensive applications in real-world scenarios due to
complex environments and limitations of imaging devices. Conventional methods
are often constrained by their tailored models, resulting in diminished
robustness when confronted with challenging degradation conditions. In
response, we propose FlowIE, a simple yet highly effective flow-based image
enhancement framework that estimates straight-line paths from an elementary
distribution to high-quality images. Unlike previous diffusion-based methods
that suffer from long-time inference, FlowIE constructs a linear many-to-one
transport mapping via conditioned rectified flow. The rectification straightens
the trajectories of probability transfer, accelerating inference by an order of
magnitude. This design enables our FlowIE to fully exploit rich knowledge in
the pre-trained diffusion model, rendering it well-suited for various
real-world applications. Moreover, we devise a faster inference algorithm,
inspired by Lagrange's Mean Value Theorem, harnessing midpoint tangent
direction to optimize path estimation, ultimately yielding visually superior
results. Thanks to these designs, our FlowIE adeptly manages a diverse range of
enhancement tasks within a concise sequence of fewer than 5 steps. Our
contributions are rigorously validated through comprehensive experiments on
synthetic and real-world datasets, unveiling the compelling efficacy and
efficiency of our proposed FlowIE. Code is available at
this https URL

FlowIE 是一个基于流的简单而高效的图像增强框架，通过估计直线路径从一个分布到高质量图像，使用条件矫正流构建线性的一对多传输映射，加速推断，实现对预训练扩散模型丰富知识的充分利用，同时还设计了一种更快的推断算法，通过使用中点切线方向来优化路径估计，取得更优的视觉效果。经过综合实验对合成和真实数据集的严格验证，展示了 FlowIE 的显著功效和高效性。

FlowIE: 通过修正流实现高效图像增强

FlowIE: Efficient Image Enhancement via Rectified Flow

Generative models have gained popularity for their potential applications in
imaging science, such as image reconstruction, posterior sampling and data
sharing. Flow-based generative models are particularly attractive due to their
ability to tractably provide exact density estimates along with fast,
inexpensive and diverse samples. Training such models, however, requires a
large, high quality dataset of objects. In applications such as computed
imaging, it is often difficult to acquire such data due to requirements such as
long acquisition time or high radiation dose, while acquiring noisy or
partially observed measurements of these objects is more feasible. In this
work, we propose AmbientFlow, a framework for learning flow-based generative
models directly from noisy and incomplete data. Using variational Bayesian
methods, a novel framework for establishing flow-based generative models from
noisy, incomplete data is proposed. Extensive numerical studies demonstrate the
effectiveness of AmbientFlow in correctly learning the object distribution. The
utility of AmbientFlow in a downstream inference task of image reconstruction
is demonstrated.

使用环境流（AmbientFlow）框架，通过变分贝叶斯方法，从噪声和不完整数据中直接学习基于流的生成模型，并通过大量数值研究证明了 AmbientFlow 在正确学习对象分布方面的有效性，并演示了 AmbientFlow 在图像重建的下游推理任务中的实用性。

AmbientFlow: 从不完整、噪声测量中生成可逆模型

AmbientFlow: Invertible generative models from incomplete, noisy  measurements

Most modern text-to-speech architectures use a WaveNet vocoder for
synthesizing high-fidelity waveform audio, but there have been limitations,
such as high inference time, in its practical application due to its ancestral
sampling scheme. The recently suggested Parallel WaveNet and ClariNet have
achieved real-time audio synthesis capability by incorporating inverse
autoregressive flow for parallel sampling. However, these approaches require a
two-stage training pipeline with a well-trained teacher network and can only
produce natural sound by using probability distillation along with auxiliary
loss terms. We propose FloWaveNet, a flow-based generative model for raw audio
synthesis. FloWaveNet requires only a single-stage training procedure and a
single maximum likelihood loss, without any additional auxiliary terms, and it
is inherently parallel due to the characteristics of generative flow. The model
can efficiently sample raw audio in real-time, with clarity comparable to
previous two-stage parallel models. The code and samples for all models,
including our FloWaveNet, are publicly available.

本文提出了 FloWaveNet，一种基于可逆流的生成模型，使用单阶段训练程序和单个最大似然损失函数，无需附加的辅助项，能实时高效地输出与先前两阶段并行模型相媲美的清晰音频