arXiv · 2611.05008
Learned Smartphone ISP on Mobile GPUs: Mobile AI 2026 Challenge Report
Abstract
With the increasing importance of mobile camera quality in modern smartphones, growing attention has been directed toward computational photography and advanced image signal processing (ISP) algorithms aimed at enhancing various perceptual attributes of captured images. This creates a need for efficient deep learning models for this problem that are not only performant but can also be deployed on constrained mobile hardware. In this Mobile AI challenge, we address this problem and propose the participants to design fast solutions that can reconstruct RGB photos from RAW Bayer camera data while running on popular smartphone GPUs from Qualcomm and Arm. For this, the participants were provided with a large-scale Fujifilm UltraISP dataset consisting of RAW-RGB image pairs captured with the Sony IMX586 Quad Bayer mobile sensor and a professional 102MP medium format FujiFilm GFX100 camera. The runtime of all models was evaluated on the Snapdragon 8 Elite Gen 5 and Dimensity 9500 chipsets with the Adreno and Mali GPUs, respectively. Top solutions proposed in this challenge are able to reconstruct high-quality 5MP RGB image from raw data on both platforms under 100 milliseconds. A comprehensive description of the models developed in the challenge is provided in this paper.
Explore related subjects
Keep this discovery
Explore connections, maps & timelines
Andrey Ignatov, Georgy Perevozchikov, Radu Timofte, Yuhong He, Yuxuan Qiu, Tianyu Hao, Ruoqi Li, Yueqi Yang, Chaoyu Feng, Dongqing Zou, Lei Lei, Na Jiang, Yongrui Zhang, Hailong Yan, Xiangtao Zhang, Ao Li, Le Zhang, Andrei Arhire, Jing Fang, XingLin Xie, YiChen Lu, Haibo Xu. 2026-11-03. Learned Smartphone ISP on Mobile GPUs: Mobile AI 2026 Challenge Report. https://arxiv.org/abs/2611.05008
Cite the original work for its findings. Save a collection to share your selection of sources.