Efficient Image Denoising on Smartphone GPUs: Mobile AI 2026 Challenge Report

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.

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)

arXiv:2611.00974 [cs.CV]

(or arXiv:2611.00974v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2611.00974 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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