PyNet-V2 Mobile: Efficient On-Device Photo Processing With Neural Networks

The increased importance of mobile photography created a need for fast and performant RAW image processing pipelines capable of producing good visual results in spite of the mobile camera sensor limitations. While deep learning-based approaches can efficiently solve this problem, their computational requirements usually remain too large for high-resolution on-device image processing. To address this limitation, we propose a novel PyNET-V2 Mobile CNN architecture designed specifically for edge devices, being able to process RAW 12MP photos directly on mobile phones under 1.5 second and producing high perceptual photo quality. To train and to evaluate the performance of the proposed solution, we use the real-world Fujifilm UltraISP dataset consisting on thousands of RAW-RGB image pairs captured with a professional medium-format 102MP Fujifilm camera and a popular Sony mobile camera sensor. The results demonstrate that the PyNET-V2 Mobile model can substantially surpass the quality of tradition ISP pipelines, while outperforming the previously introduced neural network-based solutions designed for fast image processing. Furthermore, we show that the proposed architecture is also compatible with the latest mobile AI accelerators such as NPUs or APUs that can be used to further reduce the latency of the model to as little as 0.5 second. The dataset, code and pre-trained models used in this paper are available on the project website: https://github.com/gmalivenko/PyNET-v2

I. Introduction

During the past years, mobile devices became a major source of photos taken by regular users, replacing compact point-and-shoot cameras and entry-levels DSLRs. Thus, the demand for high quality smartphone photos has also increased significantly. Lots of efforts are now being devoted to designing powerful image signal processing (ISP) pipelines capable of dealing with hardware limitations of small mobile camera sensors. As the conventional hand-crafted approaches are no longer able to provide a significant boost of image quality, more and more attention is now being paid to deep learning-based computational photography allowing to push the visual results of the processed images to a new level.