论文标题
部分可观测时空混沌系统的无模型预测
Learned Smartphone ISP on Mobile GPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Report
论文作者
论文摘要
在过去的几年中，移动相机的作用急剧增加，从而导致越来越多的自动图像质量增强和原始照片处理研究。在此移动AI挑战中，目标是开发有效的端到端AI基于AI的图像信号处理（ISP）管道，以替换可以使用Tensorflow Lite在现代智能手机GPU上运行的标准移动ISP。为参与者提供了一个大规模的富士式Ultraisp数据集，该数据集由数千张配对照片组成，这些照片与普通的移动相机传感器和专业的102MP中型Fujifilm fujifilm GFX100相机一起捕获。在Snapdragon的8代1 GPU上评估了所得模型的运行时，该运行时间为大多数常见的深度学习操作提供了出色的加速结果。所提出的解决方案与所有最近的移动GPU兼容，能够在不到20-50毫秒的情况下处理完整的高清照片，同时实现高忠诚度结果。本文提供了本挑战中所有模型的详细描述。
The role of mobile cameras increased dramatically over the past few years, leading to more and more research in automatic image quality enhancement and RAW photo processing. In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based image signal processing (ISP) pipeline replacing the standard mobile ISPs that can run on modern smartphone GPUs using TensorFlow Lite. The participants were provided with a large-scale Fujifilm UltraISP dataset consisting of thousands of paired photos captured with a normal mobile camera sensor and a professional 102MP medium-format FujiFilm GFX100 camera. The runtime of the resulting models was evaluated on the Snapdragon's 8 Gen 1 GPU that provides excellent acceleration results for the majority of common deep learning ops. The proposed solutions are compatible with all recent mobile GPUs, being able to process Full HD photos in less than 20-50 milliseconds while achieving high fidelity results. A detailed description of all models developed in this challenge is provided in this paper.
