论文标题
卷积平均值：用于照明估计的简单卷积神经网络
Convolutional Mean: A Simple Convolutional Neural Network for Illuminant Estimation
论文作者
论文摘要
我们提出卷积平均值（CM） - 一种简单而快速的卷积神经网络，用于照明估计。我们提出的方法仅需要一个小的神经网络模型（1.1K参数）和48 x 32缩略图输入图像。我们未优化的Python实现需要1 ms/图像，可以说比目前具有相似精度的当前领先解决方案快3-3750x。使用两个公共数据集，我们表明我们提出的轻量级方法提供了与当前领先方法（由数千/百万参数组成）相当的精度。
We present Convolutional Mean (CM) - a simple and fast convolutional neural network for illuminant estimation. Our proposed method only requires a small neural network model (1.1K parameters) and a 48 x 32 thumbnail input image. Our unoptimized Python implementation takes 1 ms/image, which is arguably 3-3750x faster than the current leading solutions with similar accuracy. Using two public datasets, we show that our proposed light-weight method offers accuracy comparable to the current leading methods' (which consist of thousands/millions of parameters) across several measures.
