SYENet: A Simple Yet Effective Network for Multiple Low-Level Vision Tasks with Real-time Performance on Mobile Device

With the rapid development of AI hardware accelerators, applying deep learning-based algorithms to solve various low-level vision tasks on mobile devices has gradually become possible. However, two main problems still need to be solved: task-specific algorithms make it difficult to integrate them in...

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Vydáno v:Proceedings / IEEE International Conference on Computer Vision s. 12148 - 12161
Hlavní autoři: Gou, Weiran, Yi, Ziyao, Xiang, Yan, Li, Shaoqing, Liu, Zibin, Kong, Dehui, Xu, Ke
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.10.2023
Témata:
Image resolution
Mobile handsets
Neural networks
Performance evaluation
Real-time systems
Signal processing algorithms
Throughput
ISSN:2380-7504
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Shrnutí:With the rapid development of AI hardware accelerators, applying deep learning-based algorithms to solve various low-level vision tasks on mobile devices has gradually become possible. However, two main problems still need to be solved: task-specific algorithms make it difficult to integrate them into a single neural network architecture, and large amounts of parameters make it difficult to achieve real-time inference. To tackle these problems, we propose a novel network, SYENet, with only 6K parameters, to handle multiple low-level vision tasks on mobile devices in a real-time manner. The SYENet consists of two asymmetrical branches with simple building blocks. To effectively connect the results by asymmetrical branches, a Quadratic Connection Unit(QCU) is proposed. Furthermore, to improve performance, a new Outlier-Aware Loss is proposed to process the image. The proposed method proves its superior performance with the best PSNR as compared with other networks in real-time applications such as Image Signal Processing(ISP), Low-Light Enhancement(LLE), and Super-Resolution(SR) with 2K60FPS throughput on Qualcomm 8 Gen 1 mobile SoC(System-on-Chip). Particularly, for ISP task, SYENet got the highest score in MAI 2022 Learned Smartphone ISP challenge.
ISSN:2380-7504
DOI:10.1109/ICCV51070.2023.01119