LRHDR: Learning Representation-enhanced HDR Video Reconstruction
Liao, Chenzhuo, Chen, Xin, Li, Bingchen, Meng, Yu, Yue, Tao, Hu, Xuemei
Reconstructing High Dynamic Range (HDR) video from alternately exposed Low Dynamic Range (LDR) frames is challenged by large motion, exposure-induced photometric inconsistency, and information loss in saturated or under-exposed regions. Prior HDR video pipelines typically follow an alignment-reconstruction paradigm, which is limited by the precision of alignment and the performance of the fusion module. We propose a new reconstruction framework called Learning Representation-enhanced HDR Video Reconstruction (LRHDR), which is built around two novel components: an Amalgamated Cross-exposure Consistent Representation (ACCR) network and an Adaptive Pixel-wise Sparse Weighted Fusion (APSWF). The ACCR includes an Exposure-aware Interleaved Context (EIC) encoder and a Representation Mapper (RM). The EIC couples a large-field path with a high-fidelity sub-pixel path and an exposure gate to produce exposure-aware features. The RM avoids explicit cross-exposure alignment by mapping features from different exposures into a unified representation via per-pixel, per-channel linear modulation and decoding into the calibrated linear HDR domain. The APSWF treats fusion as pixel-wise candidate selection, producing sparse weighted masks to form a normalized fusion in the linear HDR domain, thereby suppressing artifacts. Extensive experiments on standard benchmarks demonstrate that our LRHDR outperforms previous methods.
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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表