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.

翻译：随着AI硬件加速器的快速发展，基于深度学习算法解决移动设备上多种低层视觉任务逐渐成为可能。然而，当前仍存在两大核心问题：任务特定算法难以整合为统一的神经网络架构，且大量参数导致难以实现实时推理。针对这些问题，我们提出新型网络SYENet（仅需约6K参数），以实时方式处理移动设备上的多种低层视觉任务。该网络由两个非对称分支构成，采用简洁的构建模块。为有效连接非对称分支的输出结果，提出了二次连接单元（QCU）。此外，为提升性能，设计了新的离群点感知损失函数进行图像处理。实验表明，在图像信号处理（ISP）、低光照增强（LLE）和超分辨率（SR）等实时应用中，本方法在Qualcomm 8 Gen 1移动SoC上实现了2K60FPS吞吐量，并以最佳峰值信噪比（PSNR）证明了其优越性能。特别地，在ISP任务中，SYENet在MAI 2022学习型智能手机ISP挑战赛中荣获最高分。