论文标题
在移动设备上实用的深度原始图像
Practical Deep Raw Image Denoising on Mobile Devices
论文作者
论文摘要
近年来，已经对基于深度学习的图像denoising方法进行了广泛的研究，并在许多公共基准数据集中盛行。但是，统计数据在计算上太昂贵了，无法直接应用于移动设备上。在这项工作中，我们提出了一个轻巧，高效的基于神经网络的原始图像Denoiser，它可以在主流移动设备上平稳运行，并产生高质量的Denosing结果。我们的主要见解是双重的：（1）通过测量和估计传感器噪声水平，对合成传感器特定数据训练的较小网络可以超过对较大的数据的训练，该数据的较大数据是对一般数据的训练； （2）可以通过新颖的K-Sigma变换来消除不同ISO设置下的较大噪声水平变化，从而使小型网络有效地处理了广泛的噪声水平。我们进行了广泛的实验，以证明我们方法的效率和准确性。我们提议的移动友好型Denoising模型在高通Snapdragon 855芯片组上以每百万像素的70毫秒左右运行，这是2019年发行的几台旗舰智能手机的夜拍功能的基础。
Deep learning-based image denoising approaches have been extensively studied in recent years, prevailing in many public benchmark datasets. However, the stat-of-the-art networks are computationally too expensive to be directly applied on mobile devices. In this work, we propose a light-weight, efficient neural network-based raw image denoiser that runs smoothly on mainstream mobile devices, and produces high quality denoising results. Our key insights are twofold: (1) by measuring and estimating sensor noise level, a smaller network trained on synthetic sensor-specific data can out-perform larger ones trained on general data; (2) the large noise level variation under different ISO settings can be removed by a novel k-Sigma Transform, allowing a small network to efficiently handle a wide range of noise levels. We conduct extensive experiments to demonstrate the efficiency and accuracy of our approach. Our proposed mobile-friendly denoising model runs at ~70 milliseconds per megapixel on Qualcomm Snapdragon 855 chipset, and it is the basis of the night shot feature of several flagship smartphones released in 2019.
