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- W4320485770 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." @default.
- W4320485770 created "2023-02-14" @default.
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- W4320485770 date "2022-07-06" @default.
- W4320485770 modified "2023-10-16" @default.
- W4320485770 title "Learning Regularized Multi-Scale Feature Flow for High Dynamic Range Imaging" @default.
- W4320485770 doi "https://doi.org/10.48550/arxiv.2207.02539" @default.
- W4320485770 hasPublicationYear "2022" @default.
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