As the quality of mobile cameras starts to play a crucial role in modern smartphones, more and more attention is now being paid to ISP algorithms used to improve various perceptual aspects of mobile photos. In this Mobile AI challenge, the target was to develop an end-to-end deep learning-based image signal processing (ISP) pipeline that can replace classical hand-crafted ISPs and achieve nearly real-time performance on smartphone NPUs. For this, the participants were provided with a novel learned ISP dataset consisting of RAW-RGB image pairs captured with the Sony IMX586 Quad Bayer mobile sensor and a professional 102-megapixel medium format camera. The runtime of all models was evaluated on the MediaTek Dimensity 1000+ platform with a dedicated AI processing unit capable of accelerating both floating-point and quantized neural networks. The proposed solutions are fully compatible with the above NPU and are capable of processing Full HD photos under 60-100 milliseconds while achieving high fidelity results. A detailed description of all models developed in this challenge is provided in this paper.

翻译：随着移动相机的质量开始在现代智能手机中发挥关键作用,人们现在越来越注意用于改进移动照片各种感知方面的ISP算法。在移动AI挑战中,目标是开发一个端到端深的深学习图像信号处理(ISP)管道,以取代传统的手工制作的ISP,并在智能型NPU上实现近实时性能。为此,向参与者提供了一套新颖的ISP数据集,其中包括用Sony IMX586 Quad Bayer移动感应器和一个专业的102-megapixel中型摄影机拍摄的RAW-RGB图像配对。在MediaTek Dimensity 1000+平台上对所有模型的运行时间进行了评估,该平台配备了一个专门的AI处理器,能够加速浮点和孔化神经网络。拟议的解决方案与上述NUPU完全兼容,能够在60-100毫秒内处理全HD照片,同时取得高正统性结果。本文提供了在这一挑战中开发的所有模型的详细描述。