Realistic Bokeh Effect Rendering on Mobile GPUs, Mobile AI & AIM 2022
challenge: Report
- URL: http://arxiv.org/abs/2211.06769v1
- Date: Mon, 7 Nov 2022 22:42:02 GMT
- Title: Realistic Bokeh Effect Rendering on Mobile GPUs, Mobile AI & AIM 2022
challenge: Report
- Authors: Andrey Ignatov and Radu Timofte and Jin Zhang and Feng Zhang and
Gaocheng Yu and Zhe Ma and Hongbin Wang and Minsu Kwon and Haotian Qian and
Wentao Tong and Pan Mu and Ziping Wang and Guangjing Yan and Brian Lee and
Lei Fei and Huaijin Chen and Hyebin Cho and Byeongjun Kwon and Munchurl Kim
and Mingyang Qian and Huixin Ma and Yanan Li and Xiaotao Wang and Lei Lei
- Abstract summary: This challenge was to develop an efficient end-to-end AI-based rendering approach that can run on modern smartphone models.
The resulting model was evaluated on the Kirin 9000's Mali GPU that provides excellent acceleration results for the majority of common deep learning ops.
- Score: 75.79829464552311
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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