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

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arxiv 2211.06769 v1 pith:BYAON7RD submitted 2022-11-07 eess.IV cs.CV

Andrey Ignatov , Radu Timofte , Jin Zhang , Feng Zhang , Gaocheng Yu , Zhe Ma , Hongbin Wang , Minsu Kwon
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Haotian Qian Wentao Tong Pan Mu Ziping Wang Guangjing Yan Brian Lee Lei Fei Huaijin Chen Hyebin Cho Byeongjun Kwon Munchurl Kim Mingyang Qian Huixin Ma Yanan Li Xiaotao Wang Lei Lei
This is my paper · ORCID
classification eess.IVcs.CV
keywords bokehmobilechallengeeffectdeepgpusmodelsprovided
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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.

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