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arXiv · 2207.02539

Learning Regularized Multi-Scale Feature Flow for High Dynamic Range Imaging

Qian Ye·Masanori Suganuma·Jun Xiao·Takayuki Okatani
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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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Qian Ye, Masanori Suganuma, Jun Xiao, Takayuki Okatani. 2022-07-06. Learning Regularized Multi-Scale Feature Flow for High Dynamic Range Imaging. https://arxiv.org/abs/2207.02539

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