SYENet: A Simple Yet Effective Network for Multiple Low-Level Vision
  Tasks with Real-time Performance on Mobile Device
Weiran Gou Ziyao Yi Yan Xiang Shaoqing Li Zibin Liu Dehui Kong Ke Xu
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.