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Practical Deep Raw Image Denoising on Mobile Devices

  • 2020
  • OriginalPaper
  • Chapter
Published in:
Authors
Yuzhi Wang
Haibin Huang
Qin Xu
Jiaming Liu
Yiqun Liu
Jue Wang
Published in
Computer Vision – ECCV 2020
Publisher
Springer International Publishing

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Abstract

Deep learning-based image denoising approaches have been extensively studied in recent years, prevailing in many public benchmark datasets. However, the stat-of-the-art networks are computationally too expensive to be directly applied on mobile devices. In this work, we propose a light-weight, efficient neural network-based raw image denoiser that runs smoothly on mainstream mobile devices, and produces high quality denoising results. Our key insights are twofold: (1) by measuring and estimating sensor noise level, a smaller network trained on synthetic sensor-specific data can out-perform larger ones trained on general data; (2) the large noise level variation under different ISO settings can be removed by a novel k-Sigma Transform, allowing a small network to efficiently handle a wide range of noise levels. We conduct extensive experiments to demonstrate the efficiency and accuracy of our approach. Our proposed mobile-friendly denoising model runs at \(\sim \)70 ms per megapixel on Qualcomm Snapdragon 855 chipset, and it is the basis of the night shot feature of several flagship smartphones released in 2019.

Electronic supplementary material

The online version of this chapter (https://doi.org/10.1007/978-3-030-58539-6_1) contains supplementary material, which is available to authorized users.
This work is supported by The National Key Research and Development Program of China under Grant 2018YFC0831700.

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next chapter SoundSpaces: Audio-Visual Navigation in 3D Environments
Title
Practical Deep Raw Image Denoising on Mobile Devices
Authors
Yuzhi Wang
Haibin Huang
Qin Xu
Jiaming Liu
Yiqun Liu
Jue Wang
Copyright Year
2020
Book
Computer Vision – ECCV 2020

Print ISBN: 978-3-030-58538-9

Electronic ISBN: 978-3-030-58539-6

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-58539-6_1
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