We propose a novel single-shot high dynamic range (HDR) imaging algorithm based on exposure-aware dynamic weighted learning, which reconstructs an HDR image from a spatially varying exposure (SVE) raw image. First, we recover poorly exposed pixels by developing a network that learns local dynamic filters to exploit local neighboring pixels across color channels. Second, we develop another network that combines only valid features in well-exposed regions by learning exposure-aware feature fusion. Third, we synthesize the raw radiance map by adaptively combining the outputs of the two networks that have different characteristics with complementary information. Finally, a full-color HDR image is obtained by interpolating missing color information. Experimental results show that the proposed algorithm significantly outperforms conventional algorithms on various datasets. The source codes and pretrained models are available at https://github.com/viengiaan/EDWL.
CITATION STYLE
Vien, A. G., & Lee, C. (2022). Exposure-Aware Dynamic Weighted Learning for Single-Shot HDR Imaging. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13667 LNCS, pp. 435–452). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-20071-7_26
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