AIM 2020: Scene Relighting and Illumination Estimation Challenge

Helou, Majed El; Zhou, Ruofan; Süsstrunk, Sabine; Timofte, Radu; Afifi, Mahmoud; Brown, Michael S.; Xu, Kele; Cai, Hengxing; Liu, Yuzhong; Wang, Liwen; Liu, Zhisong; Li, Chu-Tak; Das, Sourya Dipta; Shah, Nisarg; Jassal, Akashdeep; Zhao, Tongtong; Zhao, Shanshan; Nathan, Sabari; Beham, M. Parisa; Suganya, R.; Wang, Qing; Hu, Zhongyun; Huang, Xin; Li, Yaning; Suin, Maitreya; Purohit, Kuldeep; Rajagopalan, A. N.; Puthussery, Densen; Hrishikesh, P. S.; Kuriakose, Melvin; Jiji, C. V.; Zhu, Yu; Dong, Liping; Jiang, Zhuolong; Li, Chenghua; Leng, Cong; Cheng, Jian

doi:10.1007/978-3-030-67070-2_30

Cited by 49 publications

(48 citation statements)

References 44 publications

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“…This challenge is one of the AIM 2020 associated challenges on: scene relighting and illumination estimation [8], image extreme inpainting [31], learned image signal processing pipeline [15], rendering realistic bokeh [16], real image super-resolution [37], efficient super-resolution [42], and video extreme superresolution [9]. Our development phase has started on May 1st, and the test phase is opened on July 10th.…”

Section: Aim 2020 Vtsr Challengementioning

confidence: 99%

AIM 2020 Challenge on Video Temporal Super-Resolution

Son¹,

Lee²,

Nah³

et al. 2020

Preprint

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Videos in the real-world contain various dynamics and motions that may look unnaturally discontinuous in time when the recorded frame rate is low. This paper reports the second AIM challenge on Video Temporal Super-Resolution (VTSR), a.k.a. frame interpolation, with a focus on the proposed solutions, results, and analysis. From low-framerate (15 fps) videos, the challenge participants are required to submit higher-frame-rate (30 and 60 fps) sequences by estimating temporally intermediate frames. To simulate realistic and challenging dynamics in the real-world, we employ the REDS VTSR dataset derived from diverse videos captured in a hand-held camera for training and evaluation purposes. There have been 68 registered participants in the competition, and 5 teams (one withdrawn) have competed in the final testing phase. The winning team proposes the enhanced quadratic video interpolation method and achieves state-of-the-art on the VTSR task.

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Section: Aim 2020 Vtsr Challengementioning

confidence: 99%

AIM 2020 Challenge on Video Temporal Super-Resolution

Son¹,

Lee²,

Nah³

et al. 2020

Preprint

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“…Previously, many methods [1,2,3] based on developing visual priors or capture properties of relighted images have achieved impressive performance. Recently, some deep learning-based methods [4,5,6,7] are proposed without explicit inverse rendering steps for estimating scene properties. However, these methods do not consider complex scenes and various ambient conditions.…”

Section: Introductionmentioning

confidence: 99%

Multi-modal Bifurcated Network for Depth Guided Image Relightin

Yang¹

2021

Preprint

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Image relighting aims to recalibrate the illumination set?ting in an image. In this paper, we propose a deep learning?based method called multi-modal bifurcated network (MB?Net) for depth guided image relighting. That is, given an image and the corresponding depth maps, a new image with the given illuminant angle and color temperature is gener?ated by our network. This model extracts the image andthe depth features by the bifurcated network in the encoder. To use the two features effectively, we adopt the dynamic dilated pyramid modules in the decoder. Moreover, to in?crease the variety of training data, we propose a novel data process pipeline to increase the number of the training data. Experiments conducted on the VIDIT dataset show that the proposed solution obtains the 1st place in terms of SSIM and PMS in the NTIRE 2021 Depth Guide One-to-one Re?lighting Challenge.

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“…This challenge is one of the AIM 2020 associated challenges on: scene relighting and illumination estimation [9], image extreme inpainting [33], learned image signal processing pipeline [22], rendering realistic bokeh [23], real image super-resolution [41], efficient super-resolution [42], video temporal superresolution [36] and video extreme super-resolution [10].…”

Section: Introductionmentioning

confidence: 99%

AIM 2020 Challenge on Learned Image Signal Processing Pipeline

Ignatov

Timofte

Zhang

et al. 2020

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This paper reviews the second AIM learned ISP challenge and provides the description of the proposed solutions and results. The participating teams were solving a real-world RAW-to-RGB mapping problem, where to goal was to map the original low-quality RAW images captured by the Huawei P20 device to the same photos obtained with the Canon 5D DSLR camera. The considered task embraced a number of complex computer vision subtasks, such as image demosaicing, denoising, white balancing, color and contrast correction, demoireing, etc. The target metric used in this challenge combined fidelity scores (PSNR and SSIM) with solutions' perceptual results measured in a user study. The proposed solutions significantly improved the baseline results, defining the state-of-the-art for practical image signal processing pipeline modeling. * A. Ignatov and R. Timofte ({andrey,radu.timofte}@vision.ee.ethz.ch, ETH Zurich) are the challenge organizers, while the other authors participated in the challenge. The Appendix A contains the authors' teams and affiliations. AIM 2020 webpage: https://data.vision.ee.ethz.ch/cvl/aim20/

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AIM 2020: Scene Relighting and Illumination Estimation Challenge

Cited by 49 publications

References 44 publications

AIM 2020 Challenge on Video Temporal Super-Resolution

AIM 2020 Challenge on Video Temporal Super-Resolution

Multi-modal Bifurcated Network for Depth Guided Image Relightin

AIM 2020 Challenge on Learned Image Signal Processing Pipeline

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