Image super-resolution is one of the most popular computer vision problems with many important applications to mobile devices. While many solutions have been proposed for this task, they are usually not optimized even for common smartphone AI hardware, not to mention more constrained smart TV platforms that are often supporting INT8 inference only. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop an end-to-end deep learning-based image super-resolution solutions that can demonstrate a real-time performance on mobile or edge NPUs. For this, the participants were provided with the DIV2K dataset and trained quantized models to do an efficient 3X image upscaling. The runtime of all models was evaluated on the Synaptics VS680 Smart Home board with a dedicated NPU capable of accelerating quantized neural networks. The proposed solutions are fully compatible with all major mobile AI accelerators and are capable of reconstructing Full HD images under 40-60 ms while achieving high fidelity results. A detailed description of all models developed in the challenge is provided in this paper.

翻译：图像超分辨率是移动设备许多重要应用程序中最受欢迎的计算机视觉问题之一。 虽然已经为此任务提出了许多解决方案, 但通常即使是通用智能AI硬件也没有优化, 更不用提往往支持INT8推断的更受限制的智能电视平台。 为了解决这个问题, 我们引入了第一个移动AI挑战, 目标是开发一个端到端深的基于学习的图像超分辨率解决方案, 该解决方案可以在移动或边缘 NPU上显示实时性能。 为此, 参与者获得了DIV2K 数据集, 并经过培训的量化模型, 以进行高效的 3X 图像升级。 所有模型的运行时间在 Synaptics VS680 智能家居板上进行了评估, 其专用的 NPU能够加速四分解神经网络 。 所提出的解决方案与所有主要的移动AI 加速器完全兼容, 并且能够在40- 60 ms 下重建完整 HD 图像, 并同时取得高度忠诚的结果 。 本文提供了在挑战中开发的所有模型的详细描述 。