AIM 2019 Challenge on Bokeh Effect Synthesis: Methods and Results

This paper reviews the first AIM challenge on bokeh effect synthesis with the focus on proposed solutions and results. The participating teams were solving a real-world image-to-image mapping problem, where the goal was to map standard narrow-aperture photos to the same photos captured with a shallo...

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Main Authors: Ignatov, Andrey, Patel, Jagruti, Timofte, Radu, Zheng, Bolun, Ye, Xin, Huang, Li, Tian, Xiang, Dutta, Saikat, Purohit, Kuldeep, Kandula, Praveen, Suin, Maitreya, Rajagopalan, A. N., Xiong, Zhiwei, Huang, Jie, Dong, Guanting, Yao, Mingde, Liu, Dong, Hong, Ming, Lin, Wenying, Qu, Yanyun, Choi, Jae-Seok, Park, Woonsung, Kim, Munchurl, Liu, Rui, Mao, Xiangyu, Yang, Chengxi, Yan, Qiong, Sun, Wenxiu, Fang, Junkai, Shang, Meimei, Gao, Fei, Ghosh, Sujoy, Sharma, Prasen Kumar, Sur, Arijit, Yang, Wenjin
Format: Conference Proceeding
Language:English
Subjects:
AIM
AIM2019
Apertures
Bokeh
Bokeh Effect
Bokeh Synthesis
Cameras
Challenge
Computer Vision
Deep Learning
Depth of Field
Image Manipulation
Kernel
Mobile Cameras
Rendering (computer graphics)
Runtime
Smartphones
Training
Visualization
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Summary:This paper reviews the first AIM challenge on bokeh effect synthesis with the focus on proposed solutions and results. The participating teams were solving a real-world image-to-image mapping problem, where the goal was to map standard narrow-aperture photos to the same photos captured with a shallow depth-of-field by the Canon 70D DSLR camera. In this task, the participants had to restore bokeh effect based on only one single frame without any additional data from other cameras or sensors. 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 baseline results, defining the state-of-the-art for practical bokeh effect simulation.
ISSN:2473-9944
DOI:10.1109/ICCVW.2019.00444