用重叠码本提升多曝光HDR重建，改善过曝区域与画质
Enhancing Multi-Exposure High Dynamic Range Imaging with Overlapped Codebook for Improved Representation Learning
- 设计重叠码本结构，共享曝光序列特征以增强隐式表示学习
- 在多个数据集上优于现有方法，有效修复过曝区域并提升视觉质量
- 适合图像重建、HDR成像领域研究者参考
高动态范围（HDR）成像技术旨在从低动态范围（LDR）输入生成逼真的HDR图像。多曝光HDR成像利用同一场景的多帧LDR图像提升重建效果，但各帧间常存在运动差异，且不同曝光设置易导致饱和区域。本文提出一种重叠码本（OLC）方案，通过共享码本结构建模常见的曝光级联过程，增强VQGAN框架对隐式HDR表示的学习能力。进一步构建新的HDR网络，利用预训练VQ网络与OLC提取的HDR表示，实现对饱和区域的补偿和整体画质提升。我们在多个数据集上进行了广泛测试，结果表明该方法在定性和定量评估上均优于以往方法。
原文摘要 · Abstract (English)
High dynamic range (HDR) imaging technique aims to create realistic HDR images from low dynamic range (LDR) inputs. Specifically, Multi-exposure HDR imaging uses multiple LDR frames taken from the same scene to improve reconstruction performance. However, there are often discrepancies in motion among the frames, and different exposure settings for each capture can lead to saturated regions. In this work, we first propose an Overlapped codebook (OLC) scheme, which can improve the capability of the VQGAN framework for learning implicit HDR representations by modeling the common exposure bracket process in the shared codebook structure. Further, we develop a new HDR network that utilizes HDR representations obtained from a pre-trained VQ network and OLC. This allows us to compensate for saturated regions and enhance overall visual quality. We have tested our approach extensively on various datasets and have demonstrated that it outperforms previous methods both qualitatively and quantitatively
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