Deploying Image Deblurring across Mobile Devices: A Perspective of
Quality and Latency
- URL: http://arxiv.org/abs/2004.12599v1
- Date: Mon, 27 Apr 2020 06:32:53 GMT
- Title: Deploying Image Deblurring across Mobile Devices: A Perspective of
Quality and Latency
- Authors: Cheng-Ming Chiang, Yu Tseng, Yu-Syuan Xu, Hsien-Kai Kuo, Yi-Min Tsai,
Guan-Yu Chen, Koan-Sin Tan, Wei-Ting Wang, Yu-Chieh Lin, Shou-Yao Roy Tseng,
Wei-Shiang Lin, Chia-Lin Yu, BY Shen, Kloze Kao, Chia-Ming Cheng, Hung-Jen
Chen
- Abstract summary: We conduct a search of portable network architectures for better quality-blur trade-off across mobile devices.
This paper provides comprehensive experiments and comparisons to uncover the in-depth analysis for both latency and image quality.
To the best of our knowledge, this is the first paper that addresses all the deployment issues of image deblurring task across mobile devices.
- Score: 11.572636762286775
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recently, image enhancement and restoration have become important
applications on mobile devices, such as super-resolution and image deblurring.
However, most state-of-the-art networks present extremely high computational
complexity. This makes them difficult to be deployed on mobile devices with
acceptable latency. Moreover, when deploying to different mobile devices, there
is a large latency variation due to the difference and limitation of deep
learning accelerators on mobile devices. In this paper, we conduct a search of
portable network architectures for better quality-latency trade-off across
mobile devices. We further present the effectiveness of widely used network
optimizations for image deblurring task. This paper provides comprehensive
experiments and comparisons to uncover the in-depth analysis for both latency
and image quality. Through all the above works, we demonstrate the successful
deployment of image deblurring application on mobile devices with the
acceleration of deep learning accelerators. To the best of our knowledge, this
is the first paper that addresses all the deployment issues of image deblurring
task across mobile devices. This paper provides practical
deployment-guidelines, and is adopted by the championship-winning team in NTIRE
2020 Image Deblurring Challenge on Smartphone Track.
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