Recovering ghost-free High Dynamic Range (HDR) images from multiple Low
Dynamic Range (LDR) images becomes challenging when the LDR images exhibit
saturation and significant motion. Recent Diffusion Models (DMs) have been
introduced in HDR imaging field, demonstrating promising performance,
particularly in achieving visually perceptible results compared to previous
DNN-based methods. However, DMs require extensive iterations with large models
to estimate entire images, resulting in inefficiency that hinders their
practical application. To address this challenge, we propose the Low-Frequency
aware Diffusion (LF-Diff) model for ghost-free HDR imaging. The key idea of
LF-Diff is implementing the DMs in a highly compacted latent space and
integrating it into a regression-based model to enhance the details of
reconstructed images. Specifically, as low-frequency information is closely
related to human visual perception we propose to utilize DMs to create compact
low-frequency priors for the reconstruction process. In addition, to take full
advantage of the above low-frequency priors, the Dynamic HDR Reconstruction
Network (DHRNet) is carried out in a regression-based manner to obtain final
HDR images. Extensive experiments conducted on synthetic and real-world
benchmark datasets demonstrate that our LF-Diff performs favorably against
several state-of-the-art methods and is 10$\times$ faster than previous
DM-based methods.

通过实现在高度压缩的潜空间中的扩散模型，并将其整合到基于回归的模型中以增强重建图像的细节，提出了无幽灵的高动态范围（HDR）成像的低频感知扩散（LF-Diff）模型。在合成和真实世界基准数据集上进行的大量实验表明，我们的 LF-Diff 比几种最先进的方法表现出更好的性能，并且比基于扩散模型的先前方法快 10 倍。

从频率视角生成 HDR 去鬼影内容

Generating Content for HDR Deghosting from Frequency View

Stack-based high dynamic range (HDR) imaging is a technique for achieving a
larger dynamic range in an image by combining several low dynamic range images
acquired at different exposures. Minimizing the set of images to combine, while
ensuring that the resulting HDR image fully captures the scene's irradiance, is
important to avoid long image acquisition and post-processing times. The
problem of selecting the set of images has received much attention. However,
existing methods either are not fully automatic, can be slow, or can fail to
fully capture more challenging scenes. In this paper, we propose a fully
automatic method for selecting the set of exposures to acquire that is both
fast and more accurate. We show on an extensive set of benchmark scenes that
our proposed method leads to improved HDR images as measured against ground
truth using the mean squared error, a pixel-based metric, and a visible
difference predictor and a quality score, both perception-based metrics.

本文介绍了一种基于堆栈的高动态范围成像技术，通过组合不同曝光下获取的低动态范围图像来实现图像的更大动态范围。该文提出了一种自动选择获取曝光时间的方法，以实现更快和更准确的 HDR 图像的获取。作者还展示了该方法在大量基准场景下的表现优于现有方法。