Shadow Inpainting and Removal Using Generative Adversarial Networks with Slice Convolutions
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Date
2019
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
In this paper, we propose a two-stage top-down and bottom-up Generative Adversarial Networks (TBGANs) for shadow inpainting and removal which uses a novel top-down encoder and a bottom-up decoder with slice convolutions. These slice convolutions can effectively extract and restore the long-range spatial information for either down-sampling or up-sampling. Different from the previous shadow removal methods based on deep learning, we propose to inpaint shadow to handle the possible dark shadows to achieve a coarse shadow-removal image at the first stage, and then further recover the details and enhance the color and texture details with a non-local block to explore both local and global inter-dependencies of pixels at the second stage. With such a two-stage coarse-to-fine processing, the overall effect of shadow removal is greatly improved, and the effect of color retention in non-shaded areas is significant. By comparing with a variety of mainstream shadow removal methods, we demonstrate that our proposed method outperforms the state-of-the-art methods.
Description
@article{10.1111:cgf.13845,
journal = {Computer Graphics Forum},
title = {{Shadow Inpainting and Removal Using Generative Adversarial Networks with Slice Convolutions}},
author = {Wei, Jinjiang and Long, Chengjiang and Zou, Hua and Xiao, Chunxia},
year = {2019},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.13845}
}