Capturing High Dynamic Range (HDR) scenery using 8-bit cameras often suffers
from over-/underexposure, loss of fine details due to low bit-depth
compression, skewed color distributions, and strong noise in dark areas.
Traditional LDR image enhancement methods primarily focus on color mapping,
which enhances the visual representation by expanding the image's color range
and adjusting the brightness. However, these approaches fail to effectively
restore content in dynamic range extremes, which are regions with pixel values
close to 0 or 255. To address the full scope of challenges in HDR imaging and
surpass the limitations of current models, we propose a novel two-stage
approach. The first stage maps the color and brightness to an appropriate range
while keeping the existing details, and the second stage utilizes a diffusion
prior to generate content in dynamic range extremes lost during capture. This
generative refinement module can also be used as a plug-and-play module to
enhance and complement existing LDR enhancement models. The proposed method
markedly improves the quality and details of LDR images, demonstrating superior
performance through rigorous experimental validation. The project page is at
this https URL

通过两阶段方法实现高动态范围（HDR）图像增强，第一阶段对颜色和亮度进行映射调整，第二阶段利用扩散先验生成在捕捉中丢失的动态范围极端处的内容，显著提高 LDR 图像的质量和细节。

Sagiri: 低动态范围图像增强与生成扩散先验

Sagiri: Low Dynamic Range Image Enhancement with Generative Diffusion  Prior

High Dynamic Range (HDR) imaging aims to replicate the high visual quality
and clarity of real-world scenes. Due to the high costs associated with HDR
imaging, the literature offers various data-driven methods for HDR image
reconstruction from Low Dynamic Range (LDR) counterparts. A common limitation
of these approaches is missing details in regions of the reconstructed HDR
images, which are over- or under-exposed in the input LDR images. To this end,
we propose a simple and effective method, HistoHDR-Net, to recover the fine
details (e.g., color, contrast, saturation, and brightness) of HDR images via a
fusion-based approach utilizing histogram-equalized LDR images along with
self-attention guidance. Our experiments demonstrate the efficacy of the
proposed approach over the state-of-art methods.

通过直方图均衡化的低动态范围图像以及自注意力引导，本文提出一种简单而有效的方法 HistoHDR-Net，用于恢复 HDR 图像的细节（如色彩、对比度、饱和度和亮度），并通过融合的方法实现。实验证明该方法优于现有技术方法。

HistoHDR-Net： 单一 LDR 转 HDR 图像的直方图均衡化

HistoHDR-Net: Histogram Equalization for Single LDR to HDR Image  Translation

Synthesizing high dynamic range (HDR) images from multiple low-dynamic range
(LDR) exposures in dynamic scenes is challenging. There are two major problems
caused by the large motions of foreground objects. One is the severe
misalignment among the LDR images. The other is the missing content due to the
over-/under-saturated regions caused by the moving objects, which may not be
easily compensated for by the multiple LDR exposures. Thus, it requires the HDR
generation model to be able to properly fuse the LDR images and restore the
missing details without introducing artifacts. To address these two problems,
we propose in this paper a novel GAN-based model, HDR-GAN, for synthesizing HDR
images from multi-exposed LDR images. To our best knowledge, this work is the
first GAN-based approach for fusing multi-exposed LDR images for HDR
reconstruction. By incorporating adversarial learning, our method is able to
produce faithful information in the regions with missing content. In addition,
we also propose a novel generator network, with a reference-based residual
merging block for aligning large object motions in the feature domain, and a
deep HDR supervision scheme for eliminating artifacts of the reconstructed HDR
images. Experimental results demonstrate that our model achieves
state-of-the-art reconstruction performance over the prior HDR methods on
diverse scenes.

本文提出一种新的基于 GAN 的模型 HDR-GAN，用于合成高动态范围（HDR）图像，并通过对抗性学习将多曝光 LDR 图像中带有缺失内容的区域恢复到原有状态，同时还提出了生成网络，并在深度 HDR 监督方案中消除了重建 HDR 图像的伪影，实验结果表明，相对于先前的 HDR 方法，在各种场景中提供了最先进的重建性能。