In this work, we present QuickSRNet, an efficient super-resolution architecture for real-time applications on mobile platforms. Super-resolution clarifies, sharpens, and upscales an image to higher resolution. Applications such as gaming and video playback along with the ever-improving display capabilities of TVs, smartphones, and VR headsets are driving the need for efficient upscaling solutions. While existing deep learning-based super-resolution approaches achieve impressive results in terms of visual quality, enabling real-time DL-based super-resolution on mobile devices with compute, thermal, and power constraints is challenging. To address these challenges, we propose QuickSRNet, a simple yet effective architecture that provides better accuracy-to-latency trade-offs than existing neural architectures for single-image super resolution. We present training tricks to speed up existing residual-based super-resolution architectures while maintaining robustness to quantization. Our proposed architecture produces 1080p outputs via 2x upscaling in 2.2 ms on a modern smartphone, making it ideal for high-fps real-time applications.

翻译：本文提出QuickSRNet，一种面向移动平台实时应用的高效超分辨率架构。超分辨率技术能增强图像清晰度、锐化细节并将其放大至更高分辨率。随着游戏、视频播放等应用以及电视、智能手机和VR头显显示能力的持续提升，高效放大解决方案的需求日益迫切。现有基于深度学习的超分辨率方法虽在视觉质量上取得显著成果，但在受计算、散热和功耗限制的移动设备上实现实时超分辨率仍具挑战。针对这些问题，我们提出QuickSRNet——一种简洁而有效的架构，在单图像超分辨率任务中实现了优于现有神经网络的精度-延迟权衡。我们提出了若干训练技巧，在保持量化鲁棒性的同时加速现有基于残差的超分辨率架构。在当代智能手机上，所提架构通过2倍放大生成1080p输出仅需2.2毫秒，特别适合高帧率实时应用场景。