NTIRE 2022在高动态范围成像上的挑战：方法和结果

论文标题

NTIRE 2022在高动态范围成像上的挑战：方法和结果

NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results

论文作者

Pérez-Pellitero, Eduardo, Catley-Chandar, Sibi, Shaw, Richard, Leonardis, Aleš, Timofte, Radu, Zhang, Zexin, Liu, Cen, Peng, Yunbo, Lin, Yue, Yu, Gaocheng, Zhang, Jin, Ma, Zhe, Wang, Hongbin, Chen, Xiangyu, Wang, Xintao, Wu, Haiwei, Liu, Lin, Dong, Chao, Zhou, Jiantao, Yan, Qingsen, Zhang, Song, Chen, Weiye, Liu, Yuhang, Zhang, Zhen, Zhang, Yanning, Shi, Javen Qinfeng, Gong, Dong, Zhu, Dan, Sun, Mengdi, Chen, Guannan, Hu, Yang, Li, Haowei, Zou, Baozhu, Liu, Zhen, Lin, Wenjie, Jiang, Ting, Jiang, Chengzhi, Li, Xinpeng, Han, Mingyan, Fan, Haoqiang, Sun, Jian, Liu, Shuaicheng, Marín-Vega, Juan, Sloth, Michael, Schneider-Kamp, Peter, Röttger, Richard, Li, Chunyang, Bao, Long, He, Gang, Xu, Ziyao, Xu, Li, Zhan, Gen, Sun, Ming, Wen, Xing, Li, Junlin, Li, Jinjing, Li, Chenghua, Gang, Ruipeng, Li, Fangya, Liu, Chenming, Feng, Shuang, Lei, Fei, Liu, Rui, Ruan, Junxiang, Dai, Tianhong, Li, Wei, Lu, Zhan, Liu, Hengyan, Huang, Peian, Ren, Guangyu, Luo, Yonglin, Liu, Chang, Tu, Qiang, Li, Fangya, Gang, Ruipeng, Li, Chenghua, Li, Jinjing, Ma, Sai, Liu, Chenming, Cao, Yizhen, Tel, Steven, Heyrman, Barthelemy, Ginhac, Dominique, Lee, Chul, Kim, Gahyeon, Park, Seonghyun, Vien, An Gia, Mai, Truong Thanh Nhat, Yoon, Howoon, Vo, Tu, Holston, Alexander, Zaheer, Sheir, Park, Chan Y.

论文摘要

本文回顾了对受约束的高动态范围（HDR）成像的挑战，该成像是与CVPR 2022结合进行的图像恢复和增强（NTIRE）研讨会的新趋势的一部分。该手稿重点介绍了竞争设置，数据集，所提出的方法及其结果。挑战旨在估算来自多个低动态范围（LDR）观测值的HDR图像，该观测值可能患有不足或过度暴露的区域和不同的噪声来源。挑战是由两个轨道组成的，重点是忠诚度和复杂性约束：在轨道1中，要求参与者优化客观的保真度得分，同时施加低复杂性约束（即解决方案不能超过给定的操作数量）。在轨道2中，要求参与者最大程度地降低解决方案的复杂性，同时对忠诚度得分施加限制（即，需要解决方案以获得比规定的基线更高的忠诚度得分）。这两种轨道都使用相同的数据和指标：通过PSNR相对于地面真相HDR图像（直接和使用规范的调整操作计算）来测量保真度，而复杂度度量标准包括多重蓄积（MAC）操作和运行时的数量（在第二秒内）。

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).

下载PDF全文

下载文献需遵守相关版权规定

论文标题