Fast Camera Image Denoising on Mobile GPUs with Deep Learning, Mobile AI
2021 Challenge: Report
- URL: http://arxiv.org/abs/2105.08629v1
- Date: Mon, 17 May 2021 13:27:56 GMT
- Title: Fast Camera Image Denoising on Mobile GPUs with Deep Learning, Mobile AI
2021 Challenge: Report
- Authors: Andrey Ignatov, Kim Byeoung-su, Radu Timofte, Angeline Pouget,
Fenglong Song, Cheng Li, Shuai Xiao, Zhongqian Fu, Matteo Maggioni, Yibin
Huang, Shen Cheng, Xin Lu, Yifeng Zhou, Liangyu Chen, Donghao Liu, Xiangyu
Zhang, Haoqiang Fan, Jian Sun, Shuaicheng Liu, Minsu Kwon, Myungje Lee,
Jaeyoon Yoo, Changbeom Kang, Shinjo Wang, Bin Huang, Tianbao Zhou, Shuai Liu,
Lei Lei, Chaoyu Feng, Liguang Huang, Zhikun Lei, Feifei Chen
- Abstract summary: We introduce the first Mobile AI challenge, where the target is to develop an end-to-end deep learning-based image denoising solution.
The proposed solutions are fully compatible with any mobile GPU and are capable of processing 480p resolution images under 40-80 ms.
- Score: 64.09439666916465
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Image denoising is one of the most critical problems in mobile photo
processing. While many solutions have been proposed for this task, they are
usually working with synthetic data and are too computationally expensive to
run on mobile devices. To address this problem, we introduce the first Mobile
AI challenge, where the target is to develop an end-to-end deep learning-based
image denoising solution that can demonstrate high efficiency on smartphone
GPUs. For this, the participants were provided with a novel large-scale dataset
consisting of noisy-clean image pairs captured in the wild. The runtime of all
models was evaluated on the Samsung Exynos 2100 chipset with a powerful Mali
GPU capable of accelerating floating-point and quantized neural networks. The
proposed solutions are fully compatible with any mobile GPU and are capable of
processing 480p resolution images under 40-80 ms while achieving high fidelity
results. A detailed description of all models developed in the challenge is
provided in this paper.
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