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.
@article{arxiv.2207.02539,
title = {Learning Regularized Multi-Scale Feature Flow for High Dynamic Range Imaging},
author = {Qian Ye and Masanori Suganuma and Jun Xiao and Takayuki Okatani},
journal= {arXiv preprint arXiv:2207.02539},
year = {2022}
}