Abstract
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.
Abstract (translated)
在本作品中,我们提出了 QuickSRNet,一个在移动设备平台上实时应用的高效超分辨率架构。超分辨率将图像澄清、锐利和拉伸到更高分辨率。例如,游戏和视频播放等应用以及电视、智能手机和虚拟现实头戴式显示器不断提高的显示能力,推动了高效超分辨率解决方案的需求。尽管现有的深度学习超分辨率方法在视觉质量方面取得了令人印象深刻的结果,但实现在具有计算、温度和功率限制的移动设备上实时深度学习超分辨率的方法仍然是具有挑战性的。为了应对这些挑战,我们提出了 QuickSRNet,一种简单但有效的架构,提供了比现有神经网络架构更好的精度和延迟权衡。我们提出了训练技巧,以加快现有残留基座超分辨率架构的速度,同时保持其对数的鲁棒性。我们提出的架构通过2倍拉伸在2.2毫秒的时间内在现代智能手机上产生1080p输出,使其成为高帧率实时应用的理想选择。
URL
https://arxiv.org/abs/2303.04336
