Exposure-Aware Dynamic Weighted Learning for Single-Shot HDR Imaging

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.