High dynamic range (HDR) imaging is of fundamental importance in modern digital photography pipelines and used to produce a high-quality photograph with well exposed regions despite varying illumination across the image. This is typically achieved by merging multiple low dynamic range (LDR) images taken at different exposures. However, over-exposed regions and misalignment errors due to poorly compensated motion result in artefacts such as ghosting. In this paper, we present a new HDR imaging technique that specifically models alignment and exposure uncertainties to produce high quality HDR results. We introduce a strategy that learns to jointly align and assess the alignment and exposure reliability using an HDR-aware, uncertainty-driven attention map that robustly merges the frames into a single high quality HDR image. Further, we introduce a progressive, multi-stage image fusion approach that can flexibly merge any number of LDR images in a permutation-invariant manner. Experimental results show our method can produce better quality HDR images with up to 1.1dB PSNR improvement to the state-of-the-art, and subjective improvements in terms of better detail, colours, and fewer artefacts.

中文翻译：

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FlexHDR：灵活 HDR 成像的对准和曝光不确定性建模


高动态范围 (HDR) 成像在现代数字摄影流程中至关重要，尽管图像中的照明不同，但仍可用于生成具有良好曝光区域的高质量照片。这通常是通过合并在不同曝光下拍摄的多个低动态范围 (LDR) 图像来实现的。然而，过度曝光的区域和由于运动补偿不良而导致的未对准误差会导致重影等伪影。在本文中，我们提出了一种新的 HDR 成像技术，专门对对准和曝光不确定性进行建模，以产生高质量的 HDR 结果。我们引入了一种策略，该策略学习使用 HDR 感知、不确定性驱动的注意力图来联合对齐和评估对齐和曝光可靠性，该注意力图将帧稳健地合并为单个高质量 HDR 图像。此外，我们引入了一种渐进式多阶段图像融合方法，可以以排列不变的方式灵活地合并任意数量的 LDR 图像。实验结果表明，我们的方法可以生成质量更好的 HDR 图像，PSNR 比最先进的水平提高了 1.1dB，并且在更好的细节、颜色和更少的伪影方面也有了主观改进。