This paper reviews the challenge on constrained high dynamic range (HDR)
imaging that was part of the New Trends in Image Restoration and Enhancement
(NTIRE) workshop, held in conjunction with CVPR 2022. This manuscript focuses
on the competition set-up, datasets, the proposed methods and their results.
The challenge aims at estimating an HDR image from 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
of two tracks with an emphasis on fidelity and complexity constraints: In Track
1, participants are asked to optimize objective fidelity scores while imposing
a low-complexity constraint (i.e. solutions can not exceed a given number of
operations). In Track 2, participants are asked to minimize the complexity of
their solutions while imposing a constraint on fidelity scores (i.e. solutions
are required to obtain a higher fidelity score than the prescribed baseline).
Both tracks use the same data and metrics: Fidelity is measured by means of
PSNR with respect to a ground-truth HDR image (computed both directly and with
a canonical tonemapping operation), while complexity metrics include the number
of Multiply-Accumulate (MAC) operations and runtime (in seconds).

该论文回顾了 2022 年 CVPR 的 NTIRE 研讨会中关于受限高动态范围（HDR）成像的挑战。本文聚焦于竞赛设定、数据集、提议的方法以及他们的结果。竞赛旨在从多个相应的低动态范围（LDR）观测中估计 HDR 图像，这些观测可能会受到欠曝或过曝区域和不同噪声来源的影响。竞赛由两个轨道组成，强调保真度和复杂性约束。在跟踪 1 中，要求参与者在强制执行低复杂性约束的同时优化客观保真度分数。在跟踪 2 中，要求参与者在强制执行保真度约束的同时最小化其解决方案的复杂度。这两个轨道使用相同的数据和指标：保真度通过与基准 HDR 图像进行相对于 PSNR 的计算来进行测量，包括通过乘积累加（MAC）操作的数量和运行时间（以秒为单位）的复杂性指标。