Transformer-guided exposure-aware fusion for single-shot HDR imaging

Abstract

Spatially varying exposure (SVE) imaging, also known as single-shot high dynamic range (HDR) imaging, is an effective and practical approach for synthesizing HDR images without the need for handling motions. In this work, we propose a novel single-shot HDR imaging algorithm using transformer-guided exposure-aware fusion to improve the exploitation of inter-channel correlations and capture global and local dependencies by extracting valid information from an SVE image. Specifically, we first extract the initial feature maps by estimating dynamic local filters using local neighbor pixels across color channels. Then, we develop a transformer-based feature extractor that captures both global and local dependencies to extract well-exposed information even in poorly exposed regions. Finally, the proposed algorithm combines only valid features in multi-exposed feature maps by learning local and channel weights. Experimental results on both synthetic and captured real datasets demonstrate that the proposed algorithm significantly outperforms state-of-the-art algorithms both quantitatively and qualitatively.