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

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

doi:10.1109/cvprw56347.2022.00114

Cited by 29 publications

(2 citation statements)

References 41 publications

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“…This challenge is one of the NTIRE 2022 associated challenges: spectral recovery [7], spectral demosaicing [6], perceptual image quality assessment [19], inpainting [40], night photography rendering [17], efficient super-resolution [31], learning the super-resolution space [34], super-resolution and quality enhancement of compressed video [49], high dynamic range [38], stereo super-resolution [45], burst super-resolution [8].…”

Section: Introductionmentioning

confidence: 99%

NTIRE 2022 Spectral Recovery Challenge and Data Set

Arad¹,

Timofte²,

Yahel³

et al. 2022

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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This paper reviews the third biennial challenge on spectral reconstruction from RGB images, i.e., the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB image. This challenge presents the "ARAD 1K" data set: a new, larger-than-ever natural hyperspectral image data set containing 1,000 images. Challenge participants were required to recover hyperspectral information from synthetically generated JPEGcompressed RGB images simulating capture by a known calibrated camera, operating under partially known parameters, in a setting which includes acquisition noise. The challenge was attended by 241 teams, with 60 teams competing in the final testing phase, 12 of which provided detailed descriptions of their methodology which are included in this report. The performance of these submissions is reviewed and provided here as a gauge for the current stateof-the-art in spectral reconstruction from natural RGB images.

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Section: Introductionmentioning

confidence: 99%

NTIRE 2022 Spectral Recovery Challenge and Data Set

Arad¹,

Timofte²,

Yahel³

et al. 2022

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Self Cite

View full text Add to dashboard Cite

show abstract

“…This challenge is one of the NTIRE 2022 associated challenges: spectral recovery [6], spectral demosaicing [5], perceptual image quality assessment [26], inpainting [46], efficient super-resolution [35], learning the super-resolution space [41], super-resolution and quality enhancement of compressed video [55], high dynamic range [44], stereo super-resolution [52], burst super-resolution [8].…”

Section: Introductionmentioning

confidence: 99%

NTIRE 2022 Challenge on Night Photography Rendering

Ershov¹,

Savchik²,

Shepelev³

et al. 2022

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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This paper reviews the NTIRE 2022 challenge on night photography rendering. The challenge solicited solutions that processed RAW camera images captured in night scenes to produce a photo-finished output image encoded in the standard RGB (sRGB) space. Given the subjective nature of this task, the proposed solutions were evaluated based on the mean opinions of viewers asked to judge the visual appearance of the results. Michael Freeman, a world-renowned photographer, further ranked the solutions with the highest mean opinion scores. A total of 13 teams competed in the final phase of the challenge. The proposed methods provided by the participating teams represent state-of-the-art performance in nighttime photography. Results from the various teams can be found here: https://nightimaging.org/

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