Can I read Feature Refinement to Improve High Resolution Image Inpainting on EtoBox?

Feature Refinement to Improve High Resolution Image Inpainting by Kulshreshtha, Prakhar; Pugh, Brian; Jiddi, Salma is a scholarly article available to read on EtoBox.

What is Feature Refinement to Improve High Resolution Image Inpainting about?

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.

Author

Kulshreshtha, Prakhar; Pugh, Brian; Jiddi, Salma

Published

2022

Language

EN