We present EngineBench, the first machine learning (ML) oriented database to
use high quality experimental data for the study of turbulent flows inside
combustion machinery. Prior datasets for ML in fluid mechanics are synthetic or
use overly simplistic geometries. EngineBench is comprised of real-world
particle image velocimetry (PIV) data that captures the turbulent airflow
patterns in a specially-designed optical engine. However, in PIV data from
internal flows, such as from engines, it is often challenging to achieve a full
field of view and large occlusions can be present. In order to design optimal
combustion systems, insight into the turbulent flows in these obscured areas is
needed, which can be provided via inpainting models. Here we propose a novel
inpainting task using random edge gaps, a technique that emphasises realism by
introducing occlusions at random sizes and orientations at the edges of the PIV
images. We test five ML methods on random edge gaps using pixel-wise,
vector-based, and multi-scale performance metrics. We find that UNet-based
models are more accurate than the industry-norm non-parametric approach and the
context encoder at this task on both small and large gap sizes. The dataset and
inpainting task presented in this paper support the development of more
general-purpose pre-trained ML models for engine design problems. The method
comparisons allow for more informed selection of ML models for problems in
experimental flow diagnostics. All data and code are publicly available at
this https URL

我们提出了 EngineBench，这是第一个以高质量实验数据为基础的机器学习（ML）导向数据库，用于研究燃烧机械内的湍流流动。通过使用实际世界的粒子图像测速（PIV）数据，EngineBench 捕捉了特殊设计的光学发动机中的湍流气流模式。我们还提出了一种新颖的修复任务，使用随机边缘缺口的修复技术来突出 PIV 图像边缘处的遮挡。我们发现 UNet 模型在这一任务上比行业常用的非参数方法和上下文编码器更准确。本文章提供的数据集和修复任务支持开发更通用的预训练 ML 模型用于发动机设计问题，并允许更明智地选择实验流动诊断问题的 ML 模型。所有数据和代码都可以在给定的链接地址公开获取。

EngineBench: 透明燃烧室中的流动重建 III 光学发动机

EngineBench: Flow Reconstruction in the Transparent Combustion Chamber  III Optical Engine

Recent advances in generative imagery have brought forth outpainting and
inpainting models that can produce high-quality, plausible image content in
unknown regions, but the content these models hallucinate is necessarily
inauthentic, since the models lack sufficient context about the true scene. In
this work, we propose RealFill, a novel generative approach for image
completion that fills in missing regions of an image with the content that
should have been there. RealFill is a generative inpainting model that is
personalized using only a few reference images of a scene. These reference
images do not have to be aligned with the target image, and can be taken with
drastically varying viewpoints, lighting conditions, camera apertures, or image
styles. Once personalized, RealFill is able to complete a target image with
visually compelling contents that are faithful to the original scene. We
evaluate RealFill on a new image completion benchmark that covers a set of
diverse and challenging scenarios, and find that it outperforms existing
approaches by a large margin. See more results on our project page:
this https URL

RealFill 是一种新的生成式修复模型，通过只使用少量与目标图像无需对齐且具有不同视角、光照条件、相机光圈或图像风格的参考图像个性化，能够以视觉上引人注目且忠实于原始场景的内容填充图像的缺失区域。在各种不同且具有挑战性的场景中，我们发现 RealFill 在新的图像修复基准测试中表现优于现有方法。

RealFill: 基于参考图像的真实图像修复生成

RealFill: Reference-Driven Generation for Authentic Image Completion

Digital art restoration has benefited from inpainting models to correct the
degradation or missing sections of a painting. This work compares three current
state-of-the art models for inpainting of large missing regions. We provide
qualitative and quantitative comparison of the performance by CoModGANs, LaMa
and GLIDE in inpainting of blurry and missing sections of images. We use
Escher's incomplete painting Print Gallery as our test study since it presents
several of the challenges commonly present in restorative inpainting.

本文研究了数字艺术修复领域中三种最先进的新兴的大面积修复模型 CoModGANs，LaMa 和 GLIDE。通过我们的测试研究 - 艾舍的不完整的画作 “图片展”，我们提供了这些模型在图像的模糊和丢失部分修复方面的定量和定性比较。