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.
翻译:图像脱色是移动照片处理中最关键的问题之一。 虽然为此任务提出了许多解决方案, 但通常使用合成数据, 且计算成本太高, 无法在移动设备上运行 。 为了解决这个问题, 我们引入了第一个移动AI 挑战, 目标是开发一个端到端的深层次基于学习的脱色图像解决方案, 能够显示智能手机GPU的高效。 为此, 向参与者提供了一套新型大型数据集, 包括野生捕捉的噪音清洁图像配对。 所有模型的运行时间都在三星Exynos 2100 芯片上进行了评估, 并配有强大的马里GPU, 能够加速浮点和量化神经网络。 拟议的解决方案与任何移动GPU完全兼容, 能够在40- 80米以下处理 480 p 分辨率图像, 并同时取得高度忠诚的结果 。 本文详细说明了在挑战中开发的所有模型 。