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Realistic Bokeh Effect Rendering on Mobile GPUs, Mobile AI & AIM 2022 challenge: Report

Authors :
Ignatov, Andrey
Timofte, Radu
Zhang, Jin
Zhang, Feng
Yu, Gaocheng
Ma, Zhe
Wang, Hongbin
Kwon, Minsu
Qian, Haotian
Tong, Wentao
Mu, Pan
Wang, Ziping
Yan, Guangjing
Lee, Brian
Fei, Lei
Chen, Huaijin
Cho, Hyebin
Kwon, Byeongjun
Kim, Munchurl
Qian, Mingyang
Ma, Huixin
Li, Yanan
Wang, Xiaotao
Lei, Lei
Publication Year :
2022

Abstract

As mobile cameras with compact optics are unable to produce a strong bokeh effect, lots of interest is now devoted to deep learning-based solutions for this task. In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based bokeh effect rendering approach that can run on modern smartphone GPUs using TensorFlow Lite. The participants were provided with a large-scale EBB! bokeh dataset consisting of 5K shallow / wide depth-of-field image pairs captured using the Canon 7D DSLR camera. The runtime of the resulting models was evaluated on the Kirin 9000's Mali GPU that provides excellent acceleration results for the majority of common deep learning ops. A detailed description of all models developed in this challenge is provided in this paper.<br />Comment: arXiv admin note: substantial text overlap with arXiv:2211.03885; text overlap with arXiv:2105.07809, arXiv:2211.04470, arXiv:2211.05256, arXiv:2211.05910

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2211.06769
Document Type :
Working Paper