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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