Efficient Image Denoising on Smartphone GPUs: Mobile AI 2026 Challenge Report
Andrey Ignatov, Georgy Perevozchikov, Radu Timofte, HaoDong Wu, Desheng Cai, Nikolay Falaleev, Faraz Kayani, Sarmad Kayani, Asad Ahmed, Dmitry Ignatov, Kihwan Yoon, Ganzorig Gankhuyag, Yoonsuk Kang, The Van Le, Jin Young Lee, Sunzhe Yang, Qiuhai Yan, Kang Chen, Tingting Wang, Faming Fang
Abstract
Image denoising is a fundamental problem in mobile photography. Despite a large number of solutions proposed for this task, the majority of them are trained on synthetically produced noisy images, and are additionally too computationally heavy for processing full-resolution images on mobile devices. This creates a need for efficient deep learning-based models 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 denoise Full HD resolution photos while running on popular smartphone GPUs from Qualcomm and Arm. For this, the participants were provided with a large-scale dataset consisting of noisy-clean image pairs captured in the wild. 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 perform high-quality image denoising under 100 milliseconds on both platforms while reaching a PSNR score of over 37 dB. A comprehensive description of the models developed in the challenge is provided in this paper.