Fast Camera Image Denoising on Mobile GPUs with Deep Learning, Mobile AI 2021 Challenge: Report
| Autor: | Haoqiang Fan, Yibin Huang, Chaoyu Feng, Zhikun Lei, Changbeom Kang, Matteo Maggioni, Donghao Liu, Shen Cheng, Liangyu Chen, Jian Sun, Shuai Xiao, Angeline Pouget, Yifeng Zhou, Myungje Lee, Radu Timofte, Fenglong Song, Tianbao Zhou, Lei Lei, Liguang Huang, Shuaicheng Liu, Feifei Chen, Shuai Liu, Zhongqian Fu, Kim Byeoung-su, Cheng Li, Shinjo Wang, Xiangyu Zhang, Jaeyoon Yoo, Andrey Ignatov, Minsu Kwon, Xin Lu, Bin Huang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Chipset Artificial neural network Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Deep learning Image and Video Processing (eess.IV) Computer Science - Computer Vision and Pattern Recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Image processing Electrical Engineering and Systems Science - Image and Video Processing Synthetic data Machine Learning (cs.LG) Pattern recognition (psychology) FOS: Electrical engineering electronic engineering information engineering Computer vision Artificial intelligence business Mobile device Image resolution |
| Zdroj: | CVPR Workshops |
| Popis: | 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. Mobile AI 2021 Workshop and Challenges: https://ai-benchmark.com/workshops/mai/2021/. arXiv admin note: substantial text overlap with arXiv:2105.07809, arXiv:2105.07825 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|