The range of real-world scene luminance is larger than the capture capability
of many digital camera sensors which leads to details being lost in captured
images, most typically in bright regions. Inverse tone mapping attempts to
boost these captured Standard Dynamic Range (SDR) images back to High Dynamic
Range (HDR) by creating a mapping that linearizes the well exposed values from
the SDR image, and provides a luminance boost to the clipped content. However,
in most cases, the details in the clipped regions cannot be recovered or
estimated. In this paper, we present a novel inverse tone mapping approach for
mapping SDR images to HDR that generates lost details in clipped regions
through a semantic-aware diffusion based inpainting approach. Our method
proposes two major contributions - first, we propose to use a semantic graph to
guide SDR diffusion based inpainting in masked regions in a saturated image.
Second, drawing inspiration from traditional HDR imaging and bracketing
methods, we propose a principled formulation to lift the SDR inpainted regions
to HDR that is compatible with generative inpainting methods. Results show that
our method demonstrates superior performance across different datasets on
objective metrics, and subjective experiments show that the proposed method
matches (and in most cases outperforms) state-of-art inverse tone mapping
operators in terms of objective metrics and outperforms them for visual
fidelity.

本文提出了一种新颖的逆向色调映射方法，通过基于语义感知的扩散补全方法在遮罩区域中生成丢失的剪切区域的细节，并借鉴传统的 HDR 成像和曝光方法，提出了一种与生成式补全方法兼容的将 SDR 补全区域提升为 HDR 图像的原则式公式。结果表明，我们的方法在客观指标上展现出卓越的性能，并且主观实验表明，所提出的方法在视觉保真度方面与最先进的逆向色调映射算子相匹配（在大多数情况下超越）和超过它们。

语义感知扩散逆色调映射

Semantic Aware Diffusion Inverse Tone Mapping

The display devices like HDR10 televisions are increasingly prevalent in our
daily life for visualizing high dynamic range (HDR) images. But the majority of
media images on the internet remain in 8-bit standard dynamic range (SDR)
format. Therefore, converting SDR images to HDR ones by inverse tone mapping
(ITM) is crucial to unlock the full potential of abundant media images.
However, existing ITM methods are usually developed with complex network
architectures requiring huge computational costs. In this paper, we propose a
lightweight Improved Residual Network (IRNet) by enhancing the power of popular
residual block for efficient ITM. Specifically, we propose a new Improved
Residual Block (IRB) to extract and fuse multi-layer features for fine-grained
HDR image reconstruction. Experiments on three benchmark datasets demonstrate
that our IRNet achieves state-of-the-art performance on both the ITM and joint
SR-ITM tasks. The code, models and data will be publicly available at
this https URL

通过改进的残差网络 (IRNet) 提出了一种轻量级的反转色调映射 (ITM) 方法，使用新的改进残差块 (IRB) 提取和融合多层特征进行高动态范围图像的重建，实验结果显示 IRNet 在 ITM 和联合 SR-ITM 任务上达到了最先进的性能水平。

高效反色调映射的轻量级改进残差网络

Lightweight Improved Residual Network for Efficient Inverse Tone Mapping

Transferring a low-dynamic-range (LDR) image to a high-dynamic-range (HDR)
image, which is the so-called inverse tone mapping (iTM), is an important
imaging technique to improve visual effects of imaging devices. In this paper,
we propose a novel deep learning-based iTM method, which learns an inverse tone
mapping network with a generative adversarial regularizer. In the framework of
alternating optimization, we learn a U-Net-based HDR image generator to
transfer input LDR images to HDR ones, and a simple CNN-based discriminator to
classify the real HDR images and the generated ones. Specifically, when
learning the generator we consider the content-related loss and the generative
adversarial regularizer jointly to improve the stability and the robustness of
the generated HDR images. Using the learned generator as the proposed inverse
tone mapping network, we achieve superior iTM results to the state-of-the-art
methods consistently.

该研究提出了一种基于深度学习和生成对抗式约束的新型逆向调色映射方法，通过交替优化的框架，使用 U-Net-based HDR 图像生成器将输入 LDR 图像转换成 HDR 图像，通过对真实 HDR 图像和生成图像的判别器进行分类，成功地实现了比现有技术更好的 iTM 效果。