标题： 使用重叠码本增强多曝光高动态范围成像的表示学习 显示英文标题

标题： Enhancing Multi-Exposure High Dynamic Range Imaging with Overlapped Codebook for Improved Representation Learning

摘要： 高动态范围（HDR）成像技术旨在从低动态范围（LDR）输入创建逼真的HDR图像。 具体来说，多曝光HDR成像使用同一场景的多个LDR帧来提高重建性能。 然而，帧之间通常存在运动差异，每次拍摄的不同曝光设置可能导致饱和区域。 在本工作中，我们首先提出了一种重叠代码本（OLC）方案，该方案可以通过在共享代码本结构中建模共同的曝光范围过程，提高VQGAN框架学习隐式HDR表示的能力。 此外，我们开发了一种新的HDR网络，该网络利用从预训练的VQ网络和OLC获得的HDR表示。 这使我们能够补偿饱和区域并提高整体视觉质量。 我们在各种数据集上对我们的方法进行了广泛测试，并证明了它在定性和定量方面都优于之前的方法。 显示英文摘要

摘要： 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