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:在这项工作中,我们展示了QuickSRNet,这是一个用于移动平台实时应用的高效超级分辨率结构。超级分辨率的澄清、放大和升级将图像升级为高分辨率。游戏和视频播放等应用,加上电视、智能手机和VR头盔的不断提高的显示能力,正在推动对高效升级解决方案的需求。虽然现有的基于深层次学习的超级分辨率方法在视觉质量方面取得了令人印象深刻的成果,但使基于实时DL的超分辨率在具有计算、热能和电力限制的移动设备上产生巨大的挑战性。为了应对这些挑战,我们提议了QuickSRNet,这是一个简单而有效的结构,比现有的超分辨率神经结构更能提供更准确到延时的交换。我们展示了加快现有基于残余的超分辨率结构的训练技巧,同时保持对四分化的稳健性。我们拟议的结构通过在2.2米的现代智能手机上以2x升空速度生成1080p产出,使其成为高速应用的理想。</s>