This paper reviews the first challenge on high-dynamic range (HDR) imaging
that was part of the New Trends in Image Restoration and Enhancement (NTIRE)
workshop, held in conjunction with CVPR 2021. This manuscript focuses on the
newly introduced dataset, the proposed methods and their results. The challenge
aims at estimating a HDR image from one or multiple respective low-dynamic
range (LDR) observations, which might suffer from under- or over-exposed
regions and different sources of noise. The challenge is composed by two
tracks: In Track 1 only a single LDR image is provided as input, whereas in
Track 2 three differently-exposed LDR images with inter-frame motion are
available. In both tracks, the ultimate goal is to achieve the best objective
HDR reconstruction in terms of PSNR with respect to a ground-truth image,
evaluated both directly and with a canonical tonemapping operation.

该论文回顾了 NTIRE 2021 高动态范围（HDR）成像挑战赛的首次挑战，着重讲述了新引入的数据集、提出的方法及其结果。该挑战旨在从一个或多个低动态范围（LDR）观测中估计 HDR 图像，要解决曝光不足或过度曝光区域和不同噪声来源的问题。本文介绍了该挑战的两个阶段，并旨在实现最佳目标 HDR 重构。

NTIRE 2021 高动态范围成像挑战赛：数据集、方法和结果

NTIRE 2021 Challenge on High Dynamic Range Imaging: Dataset, Methods and  Results

We present a CNN-based technique to estimate high-dynamic range outdoor
illumination from a single low dynamic range image. To train the CNN, we
leverage a large dataset of outdoor panoramas. We fit a low-dimensional
physically-based outdoor illumination model to the skies in these panoramas
giving us a compact set of parameters (including sun position, atmospheric
conditions, and camera parameters). We extract limited field-of-view images
from the panoramas, and train a CNN with this large set of input image--output
lighting parameter pairs. Given a test image, this network can be used to infer
illumination parameters that can, in turn, be used to reconstruct an outdoor
illumination environment map. We demonstrate that our approach allows the
recovery of plausible illumination conditions and enables photorealistic
virtual object insertion from a single image. An extensive evaluation on both
the panorama dataset and captured HDR environment maps shows that our technique
significantly outperforms previous solutions to this problem.

本文介绍了一种基于卷积神经网络的方法来从低动态范围（LDR）图像中估算高动态范围（HDR）全景图的户外照明，通过训练网络并提取大量的输入图像与输出照明参数对，本文的方法允许恢复真实感照明条件并能从单一图像中实现逼真的虚拟物体插入。