Learning Regularized Multi-Scale Feature Flow for High Dynamic Range
Imaging
- URL: http://arxiv.org/abs/2207.02539v1
- Date: Wed, 6 Jul 2022 09:37:28 GMT
- Title: Learning Regularized Multi-Scale Feature Flow for High Dynamic Range
Imaging
- Authors: Qian Ye, Masanori Suganuma, Jun Xiao, Takayuki Okatani
- Abstract summary: We propose a deep network that tries to learn multi-scale feature flow guided by the regularized loss.
It first extracts multi-scale features and then aligns features from non-reference images.
After alignment, we use residual channel attention blocks to merge the features from different images.
- Score: 29.691689596845112
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Reconstructing ghosting-free high dynamic range (HDR) images of dynamic
scenes from a set of multi-exposure images is a challenging task, especially
with large object motion and occlusions, leading to visible artifacts using
existing methods. To address this problem, we propose a deep network that tries
to learn multi-scale feature flow guided by the regularized loss. It first
extracts multi-scale features and then aligns features from non-reference
images. After alignment, we use residual channel attention blocks to merge the
features from different images. Extensive qualitative and quantitative
comparisons show that our approach achieves state-of-the-art performance and
produces excellent results where color artifacts and geometric distortions are
significantly reduced.
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