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).
翻译:本文回顾了与CVPR 2022 合作举办的图像恢复与增强新趋势讲习班(NTIRE)中限制的动态范围(HDR)成像(HDR)的挑战,这是与CVPR 2022 联合举行的图像恢复与增强新趋势讲习班的一部分。本稿侧重于竞争设置、数据集、拟议方法及其结果。挑战在于从多种不同的低动态范围观测(LDR)中估算《人类发展报告》的形象,这些观测可能受到暴露不足或过度的地区和不同噪音来源的影响。挑战由两条轨道组成,侧重于忠诚和复杂制约因素:在第1轨中,与会者被要求在强加低兼容性限制(即解决方案不能超过一定数量)的同时,优化客观忠诚度评分(即解决方案不能超过一定操作);在第轨中,与会者被要求尽可能减少其解决方案的复杂性,同时对忠诚度计数施加限制(即需要解决方案才能获得高于规定基线的更高准确度分数)。两条轨道都使用相同的数据和衡量标准:在地面人类发展报告图像方面,通过PSNRU衡量精度的度(直接和具有一定数量的操作能力),同时包括多度的硬度操作,同时包括多度操作(可视数),同时包括多度操作)。