Paper Info
Title
Fast Camera Image Denoising on Mobile GPUs with Deep Learning, Mobile AI 2021 Challenge: Report
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.
Year
DOI
Venue
2021
10.1109/CVPRW53098.2021.00285
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGITION WORKSHOPS (CVPRW 2021)
DocType
ISSN
Citations 
Conference
2160-7508
1
PageRank 
References 
Authors
0.35
26
32
Name
Order
Citations
PageRank
Andrey Ignatov1306.66
Kim Byeoung-su210.35
Radu Timofte31880118.45
Angeline Pouget410.35
Fenglong Song521.38
Cheng Li627939.13
Shuai Xiao7569.55
Zhongqian Fu810.69
Matteo Maggioni910.69
Yibin Huang1010.35
Shen Cheng1111.37
Xin Lu1251.15
Yifeng Zhou1311.70
Liangyu Chen1451.49
Donghao Liu1510.69
Xiangyu Zhang1613044437.66
Haoqiang Fan1722712.94
Jian Sun1825842956.90
Shuaicheng Liu1936328.26
Minsu Kwon2020.70
Myungje Lee2120.70
Jaeyoon Yoo2220.70
Changbeom Kang2320.70
Shinjo Wang2410.35
Bin Huang2510.35
Tianbao Zhou2610.35
Shuai Liu2720332.40
Lei Lei2810.35
Chaoyu Feng2910.35
Liguang Huang3010.35
Zhikun Lei3110.35
Feifei Chen3210.35