iHDR可处理任意数量曝光图像的迭代融合，解决传统方法固定输入数的局限。
iHDR: Iterative HDR Imaging with Arbitrary Number of Exposures
- 采用双输入融合与物理映射迭代，逐步合成高质量HDR图像。
- 在多组输入下均优于现有去鬼影HDR方法，尤其在非三张输入时优势明显。
- 适合需要灵活输入帧数的摄影与视频应用，如移动设备拍摄。
高动态范围（HDR）成像旨在通过融合多张低动态范围（LDR）图像的信息，生成高质量的HDR图像。尽管已有大量基于学习的静态与动态场景HDR方法，但其架构通常针对固定输入数量（如三张）设计，难以适用于超出预设范围的情况。为此，本文提出iHDR框架，采用迭代融合策略，包含无鬼影双输入HDR融合网络（DiHDR）和基于物理的域映射网络（ToneNet）。DiHDR利用一对输入估计中间HDR图像，ToneNet将其映射回非线性域，并作为下一轮配对融合的参考输入。该过程持续迭代，直至所有输入帧被使用。定性与定量实验表明，相比现有最先进去鬼影HDR方法，iHDR在灵活输入帧数条件下表现更优。
原文摘要 · Abstract (English)
High dynamic range (HDR) imaging aims to obtain a high-quality HDR image by fusing information from multiple low dynamic range (LDR) images. Numerous learning-based HDR imaging methods have been proposed to achieve this for static and dynamic scenes. However, their architectures are mostly tailored for a fixed number (e.g., three) of inputs and, therefore, cannot apply directly to situations beyond the pre-defined limited scope. To address this issue, we propose a novel framework, iHDR, for iterative fusion, which comprises a ghost-free Dual-input HDR fusion network (DiHDR) and a physics-based domain mapping network (ToneNet). DiHDR leverages a pair of inputs to estimate an intermediate HDR image, while ToneNet maps it back to the nonlinear domain and serves as the reference input for the next pairwise fusion. This process is iteratively executed until all input frames are utilized. Qualitative and quantitative experiments demonstrate the effectiveness of the proposed method as compared to existing state-of-the-art HDR deghosting approaches given flexible numbers of input frames.
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