Convolutional Mean: A Simple Convolutional Neural Network for Illuminant Estimation
This provides a lightweight solution for real-time color correction in imaging applications, though it is incremental as it builds on existing CNN approaches.
The paper tackles illuminant estimation by proposing Convolutional Mean, a simple and fast convolutional neural network that achieves comparable accuracy to leading methods while using only 1.1K parameters and processing images in 1 ms, which is 3-3750x faster.
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.