论文标题
功能改进以改善高分辨率图像插图
Feature Refinement to Improve High Resolution Image Inpainting
论文作者
论文摘要
在本文中，我们解决了在高分辨率上运行的神经网络质量中降解的问题。介入网络通常无法在高于其培训集的分辨率下产生全球连贯的结构。尽管图像分辨率增加，但这部分归因于静态的接收场。尽管在介入之前降低图像会产生连贯的结构，但它本质上缺乏更高分辨率的细节。为了获得两全其美，我们通过最大程度地减少推断时的多尺度一致性损失来优化网络的中间功能。此运行时优化改善了覆盖效果，并为高分辨率介绍建立了新的最先进的结果。代码可在以下网址获得：https：//github.com/geomagical/lama-with-refiner/tree/refinement。
In this paper, we address the problem of degradation in inpainting quality of neural networks operating at high resolutions. Inpainting networks are often unable to generate globally coherent structures at resolutions higher than their training set. This is partially attributed to the receptive field remaining static, despite an increase in image resolution. Although downscaling the image prior to inpainting produces coherent structure, it inherently lacks detail present at higher resolutions. To get the best of both worlds, we optimize the intermediate featuremaps of a network by minimizing a multiscale consistency loss at inference. This runtime optimization improves the inpainting results and establishes a new state-of-the-art for high resolution inpainting. Code is available at: https://github.com/geomagical/lama-with-refiner/tree/refinement.
