RefocusGAN: Scene Refocusing Using a Single Image
Abstract
Post-capture control of the focus position of an image is a useful photographic tool. Changing the focus of a single image involves the complex task of simultaneously estimating the radiance and the defocus radius of all scene points. We introduce RefocusGAN, a deblur-then-reblur approach to single image refocusing. We train conditional adversarial networks for deblurring and refocusing using wide-aperture images created from light-fields. By appropriately conditioning our networks with a focus measure, an in-focus image and a refocus control parameter, we are able to achieve generic free-form refocusing over a single image.
Cite
Text
Sakurikar et al. "RefocusGAN: Scene Refocusing Using a Single Image." Proceedings of the European Conference on Computer Vision (ECCV), 2018. doi:10.1007/978-3-030-01225-0_31Markdown
[Sakurikar et al. "RefocusGAN: Scene Refocusing Using a Single Image." Proceedings of the European Conference on Computer Vision (ECCV), 2018.](https://mlanthology.org/eccv/2018/sakurikar2018eccv-refocusgan/) doi:10.1007/978-3-030-01225-0_31BibTeX
@inproceedings{sakurikar2018eccv-refocusgan,
title = {{RefocusGAN: Scene Refocusing Using a Single Image}},
author = {Sakurikar, Parikshit and Mehta, Ishit and Balasubramanian, Vineeth N. and Narayanan, P. J.},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2018},
doi = {10.1007/978-3-030-01225-0_31},
url = {https://mlanthology.org/eccv/2018/sakurikar2018eccv-refocusgan/}
}