LRHDR：学习表示增强的HDR视频重建
LRHDR: Learning Representation-enhanced HDR Video Reconstruction
提出无需显式对齐的跨曝光一致表示学习与像素级稀疏加权融合，可复用于多帧融合、曝光不一致下的视频增强等底层视觉任务。
原文依据 The RM avoids explicit cross-exposure alignment by mapping features from different exposures into a unified representation
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论从交替曝光的低动态范围（LDR）帧重建高动态范围（HDR）视频面临大运动、曝光引起的光度不一致以及饱和或欠曝区域信息丢失等挑战。现有HDR视频流程通常遵循对齐-重建范式，受限于对齐精度和融合模块性能。我们提出一种新的重建框架——学习表示增强的HDR视频重建（LRHDR），其核心是两个新颖组件：融合跨曝光一致表示（ACCR）网络和自适应像素级稀疏加权融合（APSWF）。ACCR包含曝光感知交错上下文（EIC）编码器和表示映射器（RM）。EIC将大视野路径与高保真亚像素路径及曝光门耦合，以产生曝光感知特征。RM通过逐像素、逐通道的线性调制将不同曝光的特征映射到统一表示，并解码到校准的线性HDR域，从而避免显式跨曝光对齐。APSWF将融合视为像素级候选选择，产生稀疏加权掩码以在线性HDR域中形成归一化融合，从而抑制伪影。在标准基准上的大量实验表明，我们的LRHDR优于先前方法。
英文原文摘要 1426 字符
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.