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，一种适用于移动平台实时应用的高效超分辨率架构。超分辨率技术可对图像进行清晰化、锐化处理并提升至更高分辨率。随着游戏、视频播放等应用场景的发展，以及电视、智能手机和VR头显显示能力的持续提升，对高效上采样方案的需求日益迫切。尽管现有基于深度学习的超分辨率方法在视觉质量方面取得了显著成果，但在受计算、散热和功耗约束的移动设备上实现实时超分辨率仍具挑战。为应对这些挑战，我们提出QuickSRNet——一种简洁高效的架构，在单图像超分辨率任务中实现了比现有神经架构更优的精度-延迟权衡。我们提出了加速现有残差类超分辨率架构的训练技巧，同时保持其对量化的鲁棒性。所提架构在主流智能手机上实现2倍上采样至1080p输出仅需2.2毫秒，使其成为高帧率实时应用的理想选择。