Fast Camera Image Denoising on Mobile GPUs with Deep Learning, Mobile AI 2021 Challenge: Report

TL;DR

This paper reports on a challenge to develop efficient deep learning models for real-time image denoising on mobile GPUs, providing a large dataset and evaluating models on actual hardware to ensure practical applicability.

Contribution

It introduces the first Mobile AI challenge for mobile GPU-based image denoising, offering a new large-scale dataset and benchmarking models on real hardware for practical efficiency.

Findings

Models can process 480p images in 40-80 ms on mobile GPUs.

Proposed solutions achieve high fidelity denoising results.

All models are compatible with various mobile GPU architectures.

Abstract

Image denoising is one of the most critical problems in mobile photo processing. While many solutions have been proposed for this task, they are usually working with synthetic data and are too computationally expensive to run on mobile devices. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop an end-to-end deep learning-based image denoising solution that can demonstrate high efficiency on smartphone GPUs. For this, the participants were provided with a novel large-scale dataset consisting of noisy-clean image pairs captured in the wild. The runtime of all models was evaluated on the Samsung Exynos 2100 chipset with a powerful Mali GPU capable of accelerating floating-point and quantized neural networks. The proposed solutions are fully compatible with any mobile GPU and are capable of processing 480p resolution images under 40-80 ms while achieving high fidelity results. A detailed description of all models developed in the challenge is provided in this paper.